ROLE AND RESPONSIBILITY

AI engineering · Stage expectations

Use target responsibility to interpret evidence. These editorial profiles are not company levels or years-of-experience rules. Professional contribution and engineering management are separate paths, determined by actual authority and accountability.

The same question can reveal different evidence at different stages. The 1–4 score is calibrated within the target role and responsibility; a larger project or higher title does not automatically earn a higher score. ICs need not have direct reports.

Junior EngineerIndividual-contributor pathCompletes bounded work with clear goals and guidance, understands the user and intended outcome, verifies the output, and communicates promptly.

Junior Engineer

Professional evidence to inspect

Evaluation samples, baseline comparisons, model/retrieval/tool boundaries, recovery, quality, and cost records.

Business outcomes

Clarifies the user, goal, and acceptance conditions before starting a task.
Evidence: Can restate the user, intended change, and completion conditions.
Business context · Product judgment
Completes agreed work and promptly surfaces deviations that could affect the result.
Evidence: Deliverable meets the agreement; risks or delays are raised early.
Problem decomposition · Ownership
Uses AI only when permitted and suitable for the task; checks facts, code, and references in proportion to risk before submitting.
Evidence: Can explain the change and show execution or manual review evidence.
AI engineering · AI evaluation and governance
Checks acceptance and user feedback after delivery and follows up on unresolved issues.
Evidence: Acceptance records connect feedback to follow-up changes.
Business outcomes and validation

Domain expertise

Applies foundational role knowledge to common tasks and asks for help when uncertain.
Evidence: Work passes basic checks and key concepts can be explained.
Computer-science foundations · Coding and abstraction
Follows team quality, security, and privacy practices and checks edge cases before submission.
Evidence: Can identify obvious failure paths or sensitive-data risks.
Quality, security, and safety · Boundaries
When AI use is permitted, provides useful context, judges whether the output fits the task, and explains when it should not be used.
Evidence: Changes include verification; generated content is not assumed correct.
AI engineering · Debugging
Explains business objects, states, and data meaning in the task and checks normal and exceptional paths.
Evidence: Examples explain state transitions and data constraints.
Data model
Uses reproduction, logs, and small experiments to diagnose routine problems and brings evidence when seeking help.
Evidence: Provides reproduction steps, eliminated hypotheses, and verification records.
Debugging

Team influence

Communicates progress, blockers, and needed support clearly.
Evidence: Collaborators know the status in time to act.
Communication · Ownership
Uses review feedback to improve the work.
Evidence: Can explain why feedback was adopted or discussed.
Feedback · Learning transfer
Works within role and authorization boundaries and escalates decisions beyond them.
Evidence: Does not make unauthorized commitments affecting others or users.
Boundaries · Stakeholder influence

Learning, initiative, thinking, and resilience

Turns feedback into a concrete improvement in the next task.
Evidence: Fewer repeat issues or reuse of a new method on a related task.
Learning transfer · Structured reflection
Separates known facts, hypotheses, and unknowns when describing a problem.
Evidence: States what observations support a view and what remains unverified.
Metrics and causal reasoning · Debugging
Represents contributions, mistakes, and risks honestly and closes owned work.
Evidence: Corrects the record promptly and does not conceal issues or overstate results.
Integrity · Attribution · Ownership
Sets small learning goals for current knowledge gaps and checks whether learning improves the work.
Evidence: Learning goals connect to visible improvement in later work.
Self-direction
Describes difficulties after setbacks, seeks timely support, resumes progress, and recognizes unsustainable workload.
Evidence: Explains support sought, recovery actions, and the outcome of another attempt.
Resilience and recovery

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Mid-level EngineerIndividual-contributor pathOwns a feature or component end to end, delivers reliably, follows operational feedback, and supports peers when relevant to the role.

Mid-level Engineer

Professional evidence to inspect

Evaluation samples, baseline comparisons, model/retrieval/tool boundaries, recovery, quality, and cost records.

Business outcomes

Owns delivery of a feature and tracks whether it solves the intended user problem.
Evidence: Reviews adoption, completion, or user feedback after release and adjusts.
Business outcomes and validation · Metrics and causal reasoning
Makes evidence-based trade-offs among cost, quality, and speed within agreed goals.
Evidence: Explains the choice, alternatives, and conditions that could change the result.
Prioritization and trade-offs · Product judgment
Where role policy permits and risk is controlled, compares AI with alternatives on total cost, quality, and risk while owning the user outcome.
Evidence: Compares workflow time or quality and retains human review points.
AI engineering · Business outcomes and validation
Breaks feature goals into milestones, acceptance measures, and pause conditions and updates them with feedback.
Evidence: Plans include outcome hypotheses, dependencies, and changes.
Planning

Domain expertise

Designs a maintainable component solution and identifies interfaces, state, and major failure modes.
Evidence: Design supports requirements and explains capacity, migration, or rollback.
System design · Data model
Adds tests, observability, or security checks to important paths to reduce foreseeable failures.
Evidence: Tests or runtime signals demonstrate that key constraints are protected.
Quality, security, and safety · Reliability and incidents
When AI is used, validates generated or suggested work according to impact and risk; escalates or rejects what cannot be explained or verified.
Evidence: Evidence includes tests, comparison cases, reference checks, or human approval.
AI evaluation and governance · Debugging
Maps business rules into component data, states, and interfaces and plans compatibility and migration.
Evidence: The model explains exceptions, historical data, and migration checks.
Model evolution
Tracks a component after release, rolls back or diagnoses failures, and verifies repairs.
Evidence: Release monitoring, rollback conditions, and verified repairs form a closed loop.
Reliability and incidents

Team influence

Aligns dependencies and acceptance with product, design, and partner teams to deliver a feature.
Evidence: Decisions, dependencies, and owners are clear; changes are shared promptly.
Communication · Capacity and dependencies
Helps peers avoid repeat problems through reviews, documentation, or sharing.
Evidence: Others reuse the practice; quality issues or handoff costs decline.
Feedback · Engineering systems
Resolves collaboration disagreements by clarifying facts and constraints and reaching an actionable decision.
Evidence: The disagreement ends with a decision, owner, and review point.
Conflict navigation · Negotiation
Preserves decision context, operating instructions, and known risks so peers can take over.
Evidence: A peer completes a change or diagnosis using the handoff material.
Communication

Learning, initiative, thinking, and resilience

Changes the plan when evidence invalidates an assumption instead of defending the original approach.
Evidence: Shows how new evidence changed priority or implementation.
Structured reflection · Metrics and causal reasoning
Builds trust by keeping commitments, surfacing bad news early, and aligning expectations.
Evidence: Partners can plan around updates and face fewer surprises.
Ownership · Communication
Uses task fit, risk, and validation cost to decide when AI may assist, expert review is needed, automation should stop, or AI should not be used.
Evidence: Identifies data, access, accuracy, or impact-scope risks.
AI evaluation and governance · Boundaries
Clarifies goals, constraints, and missing facts before choosing how to investigate an ambiguous problem.
Evidence: Clarifying questions change the problem framing or investigation, with explicit limits to inference.
Debugging
Adjusts methods, pace, or support after setbacks and tests improvement rather than repeating effort.
Evidence: Explains concrete changes after failure and the next attempt's results.
Resilience and recovery

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Senior EngineerIndividual-contributor pathClarifies ambiguous problems, weighs cross-component options, and drives project outcomes while making authority and decision boundaries explicit when proposing plans or pivots.

Senior Engineer

Professional evidence to inspect

Evaluation samples, baseline comparisons, model/retrieval/tool boundaries, recovery, quality, and cost records.

Business outcomes

Turns an ambiguous request into a verifiable user or business outcome with baselines and guardrails.
Evidence: The team can explain why, how success is measured, and when to stop or adjust.
Business outcomes and validation · Product judgment
Uses user evidence, market or operational constraints to align cross-functional teams on high-value work.
Evidence: Priorities reflect opportunity cost and are revisited against outcomes.
Market and economic judgment · Prioritization and trade-offs
Assesses contribution to outcomes and distinguishes causal evidence from correlation.
Evidence: Explains metric definitions, observation window, guardrails, and attribution limits.
Metrics and causal reasoning · Attribution
Proposes phased plans within the owned project or domain, connecting opportunities, outcome measures, investment, and work declined; quarterly is one possible review cadence.
Evidence: The plan states outcome hypotheses, capacity constraints, and reprioritization triggers.
Planning
When results contradict the premise, reduces, stops, or redirects work within authority; beyond that authority, presents evidence and a recommendation to the decision owner.
Evidence: Records include pre-set conditions, evidence, and where released resources went.
Investment choices

Domain expertise

Designs across component boundaries, balancing domain rules, system constraints, evolution cost, and reversibility.
Evidence: Proposal covers boundaries, trade-offs, migration/rollback, and failure paths.
System design · Model evolution
Validates the problem with users and product partners before choosing an implementation.
Evidence: Research, prototypes, experiments, or data narrow or invalidate an approach.
Product discovery and experience design · Product judgment
Defines quality and security checks for high-impact solutions; where AI is involved, selects evaluation, human review, or non-use according to risk rather than treating adoption as a goal.
Evidence: Shows evaluation cases, monitoring/escalation conditions, privacy/access controls, or human handoff.
Quality, security, and safety · AI evaluation and governance
Sets a technical roadmap for the owned domain, balancing architecture, technical debt, and staged migration; includes AI investment only when relevant opportunities, risks, and validation conditions are clear.
Evidence: The roadmap includes dependencies, cost, risk, checkpoints, and exit options.
Engineering systems
Evolves cross-component domain and data models while preserving definitions, compatibility, and historical meaning.
Evidence: Data meaning and migration correctness remain traceable after business-rule changes.
Model evolution

Team influence

Aligns multiple teams on goals, dependencies, and decision rights without relying on reporting authority.
Evidence: Produces an accepted plan, commitments, and escalation path.
Stakeholder influence · Negotiation
Raises team-wide quality through review standards, shared components, or operating mechanisms.
Evidence: Rework, incidents, or duplicated implementations decline, not just documentation volume.
Engineering systems · Reliability and incidents
Helps peers take on larger problems through delegation with clear boundaries and feedback.
Evidence: Others deliver independently and reliance on personal rescue declines.
Growth and development · Delegation and succession
Identifies capability gaps from project direction and recommends development, collaboration, borrowing, or hiring; authorized owners make staffing and resource decisions.
Evidence: Capability gaps connect to work risks, closing actions, and review periods.
Growth and development

Learning, initiative, thinking, and resilience

Makes testable judgments under incomplete information and states confidence, risk, and disconfirming evidence.
Evidence: Decision includes explicit assumptions and signals for reconsideration.
Product judgment · Metrics and causal reasoning
Understands partners’ goals and constraints and remains direct, respectful, and trustworthy through conflict.
Evidence: Disagreements are discussable and commitments executable without avoiding hard issues.
Conflict navigation · Feedback
Reviews outcomes and personal judgment to create learning that changes later behavior.
Evidence: A later project uses the changed mechanism and can explain its effect.
Structured reflection · Learning transfer
Asks questions that distinguish competing hypotheses and prioritizes evidence that could change the decision.
Evidence: Explains which question ruled out which hypothesis and how the decision changed.
Product judgment
Recognizes systemic overload and capability limits under pressure, resets commitments, and supports team recovery.
Evidence: Early risk reporting, scope changes, and recovery outcomes are traceable.
Resilience and recovery

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Experienced Senior EngineerIndividual-contributor pathCreates sustained professional value in a critical domain or across teams, scaling impact through reusable practices and collaboration without assuming company-wide governance or formal succession duties.

Experienced Senior Engineer

Professional evidence to inspect

Evaluation samples, baseline comparisons, model/retrieval/tool boundaries, recovery, quality, and cost records.

Business outcomes

Translates domain-level business goals into sustainable product, technical, and operational outcomes.
Evidence: Roadmap connects business measures, capability investment, and long-term cost.
Planning · Business outcomes and validation · Investment choices
When carrying relevant domain responsibility, contributes market, business-model, and long-term investment judgment across opportunities; company-wide portfolio authority is not assumed.
Evidence: Decision states opportunity cost, downside scenarios, and evidence thresholds for further investment.
Market and economic judgment · Investment choices
Establishes cross-team outcome reviews that shape priorities and subsequent investment.
Evidence: Review shares definitions and attribution limits, assigns actions, and changes resource choices.
Metrics and causal reasoning · Business outcomes and validation
Translates annual direction into domain plans and quarterly checkpoints with dependencies and stop conditions.
Evidence: Long-term goals connect to near-term investment through an adjustable plan.
Planning
Evaluates total cost and actual adoption for platform, reuse, or relevant AI investments, testing long-term value assumptions without treating adoption itself as success.
Evidence: Compares maintenance, migration, adoption, and alternatives.
Market and economic judgment

Domain expertise

Sets evolvable domain architecture and professional standards while balancing product experience and engineering boundaries.
Evidence: Multiple teams adopt the approach and track quality, cost, and evolution outcomes.
System design · Model evolution · Product discovery and experience design
Builds quality evaluation proportionate to product risk; for AI systems, includes representative evaluations and regression monitoring.
Evidence: Reusable baselines, production monitoring, and clear human-intervention/rollback conditions exist.
AI evaluation and governance · Quality, security, and safety
Identifies cross-team complexity or technical debt and makes the overall system simpler and more reliable.
Evidence: Consumers repeat less work and incidents, change cost, or complexity decline.
Engineering systems · Quality, security, and safety
Coordinates cross-team technical roadmaps, sequencing architecture, data, platform, and relevant AI work with compatibility boundaries.
Evidence: The roadmap explains cross-team dependencies, migration sequence, and acceptance standards.
System design
Builds shared business semantics and data contracts across teams to prevent inconsistent local models.
Evidence: Key metrics, entities, and rules have owners and change mechanisms.
Data model

Team influence

Aligns and enables multiple teams through clear direction and shared decisions rather than personal approval.
Evidence: Cross-team leads make aligned decisions independently.
Stakeholder influence · Communication
Helps others take on more complex work through specialist coaching, review, shared assets, or delegation; multiple new owners or formal succession supply are optional evidence contingent on opportunity and authority.
Evidence: Multiple people can independently own critical areas.
Growth and development · Delegation and succession
Improves team boundaries, collaboration, or decision mechanisms to remove recurring systemic blockers.
Evidence: Collaboration cycles or dependency risk improve sustainably and multiple teams adopt the mechanism.
Team design · Conflict navigation
Works with management partners to identify domain capability gaps, knowledge diffusion needs, and key-person risks; headcount, hiring, and succession decisions depend on actual authority.
Evidence: Team capability plans support the domain roadmap and verify backup coverage.
Team design
Drives adoption of cross-team practices by addressing migration cost and feedback beyond publishing standards.
Evidence: Adoption, resistance, and improvement are tracked over time.
Stakeholder influence

Learning, initiative, thinking, and resilience

Calibrates judgment on long-term uncertainty and knows when to invest, experiment, wait, or stop.
Evidence: Across decisions, risk is calibrated and views update in time, not only explained after the fact.
Product judgment · Investment choices
Builds relationships across disciplines, locations, and interests where real disagreement is safe.
Evidence: Partners share bad news early and engage in difficult trade-offs.
Negotiation · Feedback
Updates judgments openly, incorporates outside evidence, and helps the organization revise its understanding.
Evidence: Decision records preserve original assumptions and updates for later teams to reuse.
Learning transfer · Integrity
Uses external changes and cross-domain counterexamples to challenge established organizational assumptions.
Evidence: Counterexamples lead to testable experiments or changes in direction.
Learning transfer
Maintains evidence, risk boundaries, and truthful attribution under major delivery pressure and develops sustainable alternatives.
Evidence: High-pressure decisions record boundaries, alternatives, and accountable owners.
Boundaries

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Engineering ManagerManagement pathOwns first-line management responsibilities for team outcomes, people development, collaboration, and operating mechanisms; understands the business and domain boundaries and uses professional judgment to review and delegate.

Engineering Manager

Professional evidence to inspect

Evaluation samples, baseline comparisons, model/retrieval/tool boundaries, recovery, quality, and cost records.

Business outcomes

Defines team goals, outcome measures, and quality guardrails with partners and recalibrates priorities.
Evidence: The team explains outcomes and trade-offs and adjusts based on evidence.
Business outcomes and validation · Planning
Allocates capacity across delivery, reliability, talent growth, and technical investment.
Evidence: Allocation reflects business risk and outcomes, with trade-offs recorded.
Investment choices · Capacity and dependencies
Owns team outcomes and explains how management decisions contributed and where attribution is limited.
Evidence: Distinguishes team delivery, external conditions, and personal contribution without appropriating credit.
Attribution · Metrics and causal reasoning
Plans and reviews with business partners at the cadence the decisions require, linking user opportunities, team investment, and outcome validation; the cadence can change with context.
Evidence: Plans trace to opportunity evidence, outcome measures, and resource allocation.
Planning
Renegotiates commitments when goals, capacity, or external conditions change, with clear accountability for scope cuts, deferral, or stopping.
Evidence: Business partners acknowledge revised commitments and track their consequences.
Capacity and dependencies

Domain expertise

Understands the team’s business flow, key domain rules, and data definitions; reviews product, technical, and quality risks, engaging AI experts when relevant, without being measured by personal coding depth.
Evidence: Asks the right questions, involves appropriate experts, and owns the decision.
System design · AI evaluation and governance · Quality, security, and safety
Builds testing, release, and incident-response practices proportionate to business risk; adds AI verification and human oversight when AI is used.
Evidence: Quality and recovery are measurable without excessive manager approval.
Engineering systems · Reliability and incidents
Enables product, design, and engineering to discover problems and validate outcomes end to end.
Evidence: The team tracks adoption and outcomes, not just on-time releases.
Product discovery and experience design · Business outcomes and validation
Builds technical roadmaps with technical leads, allocating capacity, cost comparison, and risk validation for technical debt, architecture, and relevant AI options.
Evidence: Technical plans align with business cadence and have owners and staged validation.
Engineering systems

Team influence

Builds capabilities for team goals through hiring, feedback, coaching, and performance management.
Evidence: Role gaps and growth goals are clear; performance decisions use sustained evidence.
Hiring · Growth and development
Designs clear goals, ownership, delegation, and decision mechanisms that reduce recurring friction.
Evidence: Issues are resolved at the right level and members have decision space.
Team design · Delegation and succession
Manages cross-team commitments, conflict, and escalation so the team can focus on key outcomes.
Evidence: Partners accept priorities and escalation paths; conflicts do not remain unresolved.
Negotiation · Stakeholder influence
Plans team roles, capability gaps, hiring and development cadence, capacity, and leadership succession.
Evidence: Staffing supports business and technical plans, with succession measures for key roles.
Delegation and succession
Addresses performance gaps and sustained overload through clear feedback, support, and review periods.
Evidence: Distinguishes goals, capability, motivation, and environment, with verified follow-up.
Frontline management

Learning, initiative, thinking, and resilience

Makes people decisions from sustained, multi-source evidence while checking bias and uncertainty.
Evidence: Can explain evidence, counterevidence, and follow-up observations.
Growth and development · Integrity
Builds relationships where mistakes, risks, and disagreement can be discussed candidly while maintaining accountability.
Evidence: Issues surface earlier and retrospectives use evidence.
Feedback · Conflict navigation
Reviews whether management mechanisms produce intended outcomes and adjusts personal behavior.
Evidence: Feedback and team signals lead to specific management changes and follow-up.
Structured reflection · Learning transfer
Uses explicit standards in promotion, performance, and resource decisions, manages conflicts of interest, and protects factual reporting.
Evidence: Comparable cases use consistent criteria, with routes for appeal and correction.
Integrity
Recognizes management blind spots and workload limits, seeks support, delegates, and restores judgment after setbacks.
Evidence: Support and delegation reduce recurring firefighting, and decision quality recovers.
Resilience and recovery

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Manager of ManagersManagement pathWithin actual authority, leads managers to deliver across teams and is accountable for resource direction and organizational capability; strategy, budget, and reorganization authority depend on the role mandate.

Manager of Managers

Professional evidence to inspect

Evaluation samples, baseline comparisons, model/retrieval/tool boundaries, recovery, quality, and cost records.

Business outcomes

Within organizational mandate, translates business direction into multi-team outcomes, portfolio priorities, and adjustable investment plans; executive strategy or budget authority is not assumed.
Evidence: Team goals form a coherent portfolio and outcomes/resources are revisited regularly.
Planning · Investment choices · Business outcomes and validation
Makes organization-level trade-offs across growth, efficiency, reliability, safety, and long-term capability.
Evidence: Names downside scenarios, risk owners, and conditions for changing course.
Market and economic judgment · Quality, security, and safety
Establishes consistent outcome measurement and review so leaders adjust direction using evidence.
Evidence: Cross-team measures are interpretable; attribution limits and misses are transparent.
Metrics and causal reasoning · Business outcomes and validation
Within actual authority, connects annual direction, phased portfolios, and recurring reviews while making assumptions, resource limits, and exit conditions explicit; cadence follows the decision cycle.
Evidence: Planning horizons are traceable, and cross-team conflicts can trigger portfolio changes.
Planning
Stops ineffective investments when business direction or portfolio assumptions fail and manages resource shifts and team transitions.
Evidence: Stopping and redirection include prior evidence, transition cost, and subsequent results.
Investment choices

Domain expertise

Ensures clear ownership and controls for critical technical, product, privacy, and security risks, adding proportionate evaluation and oversight when AI is involved.
Evidence: Critical risks have decision owners, monitoring, review, and response mechanisms.
AI evaluation and governance · Quality, security, and safety
Sets cross-team quality and operational standards proportionate to business risk.
Evidence: Standards reduce incidents or regressions without unnecessary process burden.
Engineering systems · Reliability and incidents
Engages experts on long-term domain and technical choices and understands business, user, and organizational consequences.
Evidence: Can compare paths and reversibility without substituting for specialists on details.
System design · Model evolution · Product judgment
Governs cross-domain technical roadmaps, connecting platform, architecture, and data investment to business direction, and evaluates AI investment and alternatives where relevant.
Evidence: The portfolio has expert review, dependency sequencing, and long-term maintenance ownership.
Engineering systems

Team influence

Within actual authority, hires, coaches, and evaluates managers and establishes management calibration and leadership handoff practices; organizational scale and succession outcomes depend on opportunity and mandate.
Evidence: Managers independently own team results and grow the next layer of leaders.
Manager of managers · Delegation and succession
Adapts organizational boundaries, decision rights, and collaboration to business and architecture changes.
Evidence: Reduces structural dependencies and local optimization; ownership and resources match goals.
Team design · Delegation and succession
Builds organization-wide mechanisms for strategic communication, conflict resolution, and decisions.
Evidence: Key trade-offs have clear decision owners and teams understand shared direction and limits.
Stakeholder influence · Conflict navigation
Plans organizational capability, key roles, and management spans with relevant decision-makers; headcount, budget, and formal succession decisions depend on actual authority.
Evidence: Organizational plans support the business portfolio and review capability gaps against outcomes.
Team design
Leads reorganizations, leadership handoffs, and resource contraction while maintaining delivery and testing new accountabilities.
Evidence: Transitions include communication, handoffs, risk protection, and organizational review.
Manager of managers

Learning, initiative, thinking, and resilience

Makes careful decisions under high uncertainty and competing interests, owning consequences and correction.
Evidence: Decisions account for long-term risk, ethics, and counterevidence, not only short-term metrics.
Product judgment · Integrity
Builds organizational trust and candor so information travels upward and bad news surfaces early.
Evidence: Risks from different levels reach decisions and commitments remain transparent.
Communication · Feedback
Turns reviews and external change into organizational capability renewal rather than one-off action items.
Evidence: Practices evolve with evidence and learning transfers across teams.
Learning transfer · Structured reflection
Builds independent feedback and checks on major people and resource decisions to prevent concentrated information and interests.
Evidence: Escalation, dissent, and correction mechanisms are demonstrably usable.
Boundaries
Maintains truthful information and decision quality through major failure or prolonged crisis and rebuilds organizational recovery capacity.
Evidence: Crisis reviews explain recovery pacing, support arrangements, and mechanism changes.
Resilience and recovery

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This role’s stage × A–H responsibility depth

Editorial responsibility anchors, not measured mastery, company levels, or the 1–4 response score. Read within the role-relevant leaves. Integrity and safety apply at every stage; higher responsibility adds prevention and correction scope.

Read the depth labels

0% · Not a general stage expectation: Not a general expectation for this stage; assess it when the role carries this responsibility.
10% · Understand boundaries and participate: Recognizes the responsibility and escalation boundary; participates with guidance without owning the outcome.
25% · Complete bounded work with guidance: Explains fundamentals, recognizes boundaries, and completes defined work with guidance.
50% · Own routine responsibilities independently: Handles routine problems independently, explains trade-offs, and verifies results.
75% · Handle complex work and broaden influence: Handles ambiguous or cross-team problems, improves shared practices, and enables others to own work.
100% · Own organizational capability: Owns capability building for an organization or critical domain, establishes calibration, and develops other owners.

Open a cell for behavior and evidence. Percentages are ordinal navigation labels and are not summed.
Stage	AProfessional problem solving and implementation	BBusiness understanding and domain modeling	CValue judgment and direction setting	DDelivery and ongoing operations	ECollaboration, communication, and influence	FPeople and organization building	GLearning, adaptation, and sustained growth	HProfessional responsibility and work style
Junior Engineer	50% Details Uses foundational knowledge on bounded tasks, asks for help when uncertain, and checks common failure paths. Evidence：Task boundaries, verification steps, issues found, and when help was sought.	10% Details Explains the users, business objects, states, and data meaning involved in the task. Evidence：Whether the task is restated through real objects and states, not only implementation steps.	10% Details Confirms why the task matters and what completion means; asks when the goal is unclear. Evidence：The user, intended change, acceptance conditions, and adjustments after clarification.	50% Details Completes agreed work, verifies the output, and surfaces blockers early. Evidence：Commitments, verification, surfaced deviations, and follow-through.	10% Details Shares progress, blockers, and needed support while following review and collaboration boundaries. Evidence：Whether updates were timely and actionable, and how feedback changed the work.	0% Details Not a general stage requirement; when relevant, participates in peer support with guidance without owning organization-building outcomes. Evidence：Check whether the role actually carries this duty and what the participation involved; lack of opportunity is insufficient evidence.	25% Details Turns feedback into an observable improvement on the next task and seeks help when stuck. Evidence：Changes between attempts, feedback source, and effect of the adjustment.	25% Details Truthfully reports facts, errors, risks, and personal contribution; baseline obligations apply at every stage. Evidence：Reporting, escalation, and correction when errors or uncertainty arise.
Mid-level Engineer	50% Details Independently designs and maintains a component, diagnoses routine issues, and verifies key quality constraints. Evidence：Design trade-offs, diagnosis, tests, and operational feedback.	25% Details Maps component business rules to data, states, and interfaces, including exceptions and compatibility. Evidence：Mapping from rules to models/interfaces, migration considerations, and exception cases.	25% Details Within a clear goal, compares value, cost, and timing, then adjusts a local plan using feedback. Evidence：Option comparisons, acceptance measures, stop conditions, and reasons for plan changes.	75% Details Owns a feature or component end to end, from decomposition and dependency coordination to release feedback and verified fixes. Evidence：Milestones, dependencies, release outcomes, rollback/fixes, and feedback follow-through.	25% Details Aligns dependencies and acceptance with product, design, and upstream/downstream partners; resolves routine disagreements. Evidence：Partners, facts behind disagreements, agreements, and handoff outcomes.	10% Details When relevant, contributes through mentoring, review, or sharing; hiring, performance, and people management are not assumed. Evidence：Authority, who was supported, actual contribution, and changes in the other person’s independence.	50% Details Checks whether a new method transfers through work outcomes and changes approach or investment when assumptions fail. Evidence：Attempts, feedback, outcomes, and reasons for changing repeated effort.	50% Details Keeps commitments for owned components, surfaces risks early, and accurately describes personal and collaborators’ contributions. Evidence：Risk timing, changed commitments, ownership, and corrective action.
Senior Engineer	75% Details Compares options in ambiguous or cross-component problems, resolves key constraints, and verifies evolution outcomes. Evidence：Constraints, alternatives, validation, and signals guiding later evolution.	50% Details Clarifies cross-component concepts, rules, and ownership boundaries, then drives coherent, explainable model evolution. Evidence：Semantic conflicts, decision rights, definitions, and compatibility choices.	50% Details Frames project problems and outcome hypotheses, proposes priorities and phases, and recommends pivots; resource decisions follow actual authority. Evidence：Distinction between recommendation and decision, measures, investment, and pivot conditions.	75% Details Drives complex cross-component delivery, manages risk and change, and checks sustained post-launch outcomes. Evidence：Risk handling, dependency changes, recovery plans, and sustained outcomes.	75% Details Secures cross-team commitments and decisions and resolves conflict without relying on reporting authority. Evidence：Stakeholder interests and constraints, treatment of dissent, and how decisions were carried out.	50% Details Uses coaching, review, and delegation to help others own larger problems; formal people authority depends on the role. Evidence：How others’ ownership changed, ongoing feedback, and alternative evidence of professional influence.	75% Details Tests judgments and disconfirming evidence under uncertainty, turning failure reviews into reusable follow-up practices. Evidence：Initial assumptions, counterevidence, changes in judgment, and later practice changes.	75% Details Handles responsibility conflicts under pressure, protects factual attribution, and drives correction. Evidence：Authority boundaries, risks affecting decisions, attribution evidence, and correction outcomes.
Experienced Senior Engineer	100% Details Provides deep judgment in a critical domain, resolves high-impact problems, and makes results reusable and evolvable. Evidence：Domain constraints, key judgments, reuse, and long-term quality outcomes.	75% Details Resolves semantics and data contracts across important domains to support sound decisions and long-term evolution. Evidence：Shared definitions, conflict resolution, compatibility, and adoption feedback.	75% Details Connects domain direction to long-term value and weighs investment, opportunity cost, and validation order; company-wide portfolio ownership is not assumed. Evidence：Domain goals, alternative investments, staged validation, and stop conditions.	75% Details Improves delivery and operations in a critical domain or across teams, reducing reliance on individual heroics. Evidence：Practice adoption, systemic bottleneck changes, and evidence teams can operate independently.	100% Details Helps multiple teams reach shared judgments and adoption paths while addressing migration cost and lasting disagreement. Evidence：Participants, decision rights, adoption friction, and actual uptake.	75% Details Scales capability through specialist coaching, shared assets, standards, or developing owners; direct-team and formal succession duties depend on authority. Evidence：Alternative evidence such as coaching, shared practice, complex problem solving, and reusable assets.	75% Details Tests domain assumptions and shares useful methods, validating learning through later decisions. Evidence：Changed assumptions, external evidence, and transfer into practice.	75% Details Maintains factual, risk, and attribution boundaries in high-impact professional decisions and proposes sustainable corrections. Evidence：Risk escalation, disputed evidence, conflicts of interest, and sustained correction.
Engineering Manager	50% Details Reviews key technical and quality risks, engages the right experts, and delegates well; personal coding depth does not substitute for management responsibility. Evidence：How experts were engaged, assumptions challenged, and risks followed through.	50% Details Understands the team’s business flow, domain rules, and data definitions, and checks boundaries that affect outcomes. Evidence：Judgments about business objects, data definitions, dependencies, and their effects.	75% Details Turns business goals into team outcomes and capacity choices, calibrating commitments and priorities with partners. Evidence：Goals, capacity, guardrails, trade-offs, and partner-aligned commitments.	75% Details Uses team mechanisms to manage capacity, dependencies, quality, and operational risk, correcting course without relying on personal heroics. Evidence：Operating cadence, risk handling, delegation, and team outcomes.	75% Details Manages partner expectations, team commitments, and escalation paths while protecting candid feedback and effective collaboration. Evidence：Conflict decisions, reset expectations, feedback channels, and fulfilled commitments.	100% Details Owns first-line management responsibilities for hiring, performance, development, delegation, and team health. Evidence：Evidence, fairness, support, and follow-up checks behind people decisions.	50% Details Uses team signals to inspect management practices, validate changes, and adjust personal management behavior. Evidence：Team feedback, management changes, observation period, and outcomes.	75% Details Maintains facts, fairness, and accountability in people and resource decisions, with effective correction paths. Evidence：Decision authority, applicable standards, dissent handling, and corrections.
Manager of Managers	25% Details Can discuss critical technical choices with experts, raise constraints and risk questions, and understand business consequences without implementing or reviewing every specialty in depth. Evidence：How expert judgment is engaged, key assumptions are tested, and unsupported endorsement is avoided.	75% Details Identifies structural conflicts across domains and aligns experts on shared semantics, data definitions, and change ownership. Evidence：Cross-domain conflicts, business consequences, shared definitions, and decision rights.	100% Details Combines direction and resources across teams within delegated authority and adjusts the portfolio based on outcomes; strategy and budget authority depend on mandate. Evidence：Mandate, portfolio options, investment shifts, and business outcomes.	75% Details Calibrates cross-team delivery and operations, addressing systemic bottlenecks, crises, and organizational transitions. Evidence：Cross-team bottlenecks, operating measures, crisis handling, and transition continuity.	100% Details Establishes reliable mechanisms with managers and key partners for direction, decision rights, escalation, and dissent. Evidence：How the leadership group handles dissent, bad news, and shared decisions.	100% Details Hires, coaches, and evaluates managers, calibrating management quality, organizational boundaries, capability allocation, and succession risk. Evidence：Manager development and assessment, cross-team capability gaps, succession risks, and organizational outcomes.	75% Details Brings multi-team reviews and external changes into organizational decisions and checks whether learning transfers into practice. Evidence：Recurring problems, mechanism changes, adoption, and recurrence.	100% Details Within delegated authority, establishes upward information flow, checks on power, and correction paths for consequential decisions. Evidence：Independent feedback, conflict-of-interest controls, decision review, and correction.