Software Engineering
ML/AI
You'll architect systems that learn and adapt, not just execute code. This path creates CTOs who see patterns in chaos and build organizations that evolve with their data.
The Career Arc
Rotational · L1–L3
Build the ML/AI craft. Prove you can wield the tools of Software Engineering.
Transformational · L4–L7
Deliver ML/AI outcomes — each Software Engineering tour at this altitude has a defined mission and success criteria.
- L4 : Lead ML projects and mentor others
- L5 : Drive ML architecture across the org
- L6 : Set ML engineering direction company-wide
- L7 : Shape the company's ML vision
Manage a Team?
Great ML/AI managers are practitioners first. The Software Engineering IC responsibilities in L4–L7 are your foundation — your management responsibilities are additive:
- • Run 1:1s for growth, not status—development conversations, not standups
- • Hire engineers who raise the bar—own the interview process end-to-end
- • Give direct feedback with care—it's how people actually improve
- • Remove blockers—shield the team from chaos so they can ship
- • Watch for burnout and team health—intervene before it's a crisis
Foundational · L8–L9
Shape the Software Engineering organization from the ML/AI chair — build institutions, not just products.
L1 — Associate ML Engineer Rotational Full detail →
Mission
Learn ML engineering through model deployment
This tour of duty
Deploy your first model to production
Own the outcomes
- • Learn ML engineering fundamentals including pipelines, models, and deployment
- • Build simple ML pipeline components with guidance from senior engineers
- • Write tests for ML code including data validation and model evaluation
- • Document ML systems and model behaviors
- • Support model monitoring and performance tracking
- • Participate in ML design reviews to learn MLOps patterns
ML/AI at L1 — the competency bar
AI in this role
- • Generating model training code
- • Debugging inference issues
- • Writing model tests
L2 — Junior ML Engineer Rotational Full detail →
Mission
Deploy and maintain ML models in production
This tour of duty
Own an ML system that serves real traffic
Own the outcomes
- • Implement ML pipeline features independently following team patterns
- • Debug model performance issues and data quality problems
- • Write comprehensive tests for ML pipelines and inference systems
- • Design simple ML features with appropriate evaluation metrics
- • Contribute to feature engineering and data pipeline maintenance
- • Support model deployment and monitoring in production
ML/AI at L2 — the competency bar
AI in this role
- • Drafting pipeline architectures
- • Analyzing model performance
- • Generating documentation
L3 — Senior ML Engineer Rotational Full detail →
Mission
Own ML systems with strong MLOps practices
This tour of duty
Lead a project that improves model performance or reliability
Own the outcomes
- • Own ML systems end-to-end from training through production monitoring
- • Design ML pipelines for medium-complexity models and features
- • Identify and resolve model drift, bias, and performance issues
- • Lead technical discussions for ML implementation approaches
- • Mentor junior engineers on ML engineering best practices
- • Drive model quality improvements with measurable impact
ML/AI at L3 — the competency bar
AI in this role
- • Modeling ML system architectures
- • Reviewing ML code
- • Creating experiment frameworks
L4 — Staff ML Engineer / Manager, Engineering Transformational Full detail →
Mission
Lead ML projects and mentor others
This tour of duty
Design ML infrastructure that teams build on
Own the outcomes
- • Lead ML projects spanning multiple models or product features
- • Design ML architectures that scale with data and model complexity
- • Mentor engineers on ML systems thinking and production patterns
- • Define ML engineering standards and MLOps practices for teams
- • Drive cross-team ML infrastructure and tooling decisions
- • Own reliability and fairness for critical ML systems
ML/AI at L4 — the competency bar
AI in this role
- • Designing ML platforms
- • Analyzing ML patterns
- • Generating technical specs
L5 — Senior Staff ML Engineer / Senior Manager, Engineering Transformational Full detail →
Mission
Drive ML architecture across the org
This tour of duty
Drive ML architecture decisions across the org
Own the outcomes
- • Drive ML architecture decisions that affect the organization
- • Design ML platforms including feature stores and model serving
- • Define ML engineering standards and best practices org-wide
- • Lead evaluation of ML frameworks, tools, and infrastructure
- • Mentor senior engineers and shape ML engineering culture
- • Solve the hardest ML engineering challenges at scale
ML/AI at L5 — the competency bar
AI in this role
- • Evaluating ML technologies
- • Building MLOps documentation
- • Creating roadmaps
L6 — Director, ML Engineering Transformational Full detail →
Mission
Set ML engineering direction company-wide
This tour of duty
Define ML standards that shape engineering practices
Own the outcomes
- • Set technical direction for ML engineering across the company
- • Define ML technology strategy and multi-year roadmap
- • Establish standards that ensure reliable, fair ML systems
- • Drive technical alignment on ML platform investments
- • Represent ML engineering in executive technical discussions
- • Shape the vision for ML infrastructure evolution
ML/AI at L6 — the competency bar
AI in this role
- • Analyzing ML patterns at scale
- • Generating standards
- • Building knowledge bases
L7 — Distinguished Engineer, ML Transformational Full detail →
Mission
Shape the company's ML vision
This tour of duty
Solve an ML problem that unlocks new capabilities
Own the outcomes
- • Shape the company's ML engineering vision and long-term strategy
- • Solve industry-level ML infrastructure challenges at scale
- • Define principles that guide ML engineering decisions
- • Influence industry standards for ML systems and MLOps
- • Mentor directors and senior ML engineering leaders
- • Drive ML innovation that creates competitive advantage
ML/AI at L7 — the competency bar
AI in this role
- • Modeling ML evolution
- • Analyzing research trends
- • Creating vision documents
L8 — VP of Engineering, ML Foundational Full detail →
Mission
Build and lead ML engineering teams
This tour of duty
Build an ML team that ships reliable AI features
Own the outcomes
- • Build and lead ML engineering teams that ship reliable AI features
- • Define organizational structure for ML engineering
- • Establish hiring standards for ML engineers
- • Create the operating model for ML engineering excellence
- • Partner with research leadership on ML investments
- • Develop ML engineering managers and technical leaders
ML/AI at L8 — the competency bar
AI in this role
- • Building ML dashboards
- • Analyzing team patterns
- • Creating hiring frameworks
L9 — SVP of Engineering, ML Foundational Full detail →
Mission
Own ML strategy and execution
This tour of duty
Transform ML engineering practices across the org
Own the outcomes
- • Own ML engineering strategy and execution organization-wide
- • Define multi-year roadmap for ML platforms and infrastructure
- • Build culture that attracts top ML engineering talent
- • Partner with executives on AI product strategy
- • Establish ML engineering as competitive differentiator
- • Shape the future of ML engineering at the company
ML/AI at L9 — the competency bar
AI in this role
- • Modeling ML scenarios
- • Building strategy documents
- • Designing knowledge infrastructure
What Hiring Managers Look For
L1-L3: Demonstrates ability to implement production ML models with measurable business impact, not just research prototypes.
L4-L6: Shows track record of building ML infrastructure that scaled across multiple teams and reduced model deployment time by 50%+.
L7+: Proves capability to architect AI strategy that generated 8-figure revenue or cost savings while managing technical debt and regulatory compliance.
Common Career Transitions
ML Engineering → Data Platform at L5-L6 for broader infrastructure ownership
AI Research → Product Management at L6+ leveraging deep technical credibility
ML Infrastructure → Principal Architect at L6-L7 for cross-domain system design
Official Classifications
| System | Code | Official Title |
|---|---|---|
| O*NET-SOC (US) | 15-2051.00 | Data Scientists |
| ISCO-08 (UN/ILO) | 2511 | Systems Analysts |
| ESCO (EU) | — | Data scientist |
| SSOC 2024 (Singapore) | 25111 | Systems designer/analyst |
| NCO-2015 (India) | 2511.0100 | Systems Analyst |
At L6 and above, the manager classification 1330 — Information and Communications Technology Services Managers applies IN ADDITION to the professional code — a manager is a superset of the individual contributor, never a replacement.
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