Data Engineering
ML Engineering
You architect systems that turn raw data into intelligent decisions at enterprise scale. This path breeds technical leaders who speak both machine learning and business strategy fluently.
The Career Arc
Rotational · L1–L3
Build the ML Engineering craft. Prove you can wield the tools of Data Engineering.
Transformational · L4–L7
Deliver ML Engineering outcomes — each Data 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
- L7 : Shape the company's ML vision
Manage a Team?
Great ML Engineering managers are practitioners first. The Data Engineering IC responsibilities in L4–L7 are your foundation — your management responsibilities are additive:
- • Hire people you'd trust locked in a room—skills matter, but trust matters more
- • Build a team that balances technical depth with business curiosity
- • Run 1:1s that connect data work to business impact—not just sprint tickets
- • Give feedback that builds both technical and communication skills
- • Remove blockers—fight for data access, compute resources, and stakeholder clarity
Foundational · L8–L9
Shape the Data Engineering organization from the ML Engineering chair — build institutions, not just products.
L1 — Associate ML Engineer Rotational
Mission
Learn ML engineering through model deployment
This tour of duty
Deploy your first model to production
Own the outcomes
- • Learn ML engineering fundamentals and deployment patterns
- • Write ML pipeline code under engineer guidance
- • Support model training and evaluation workflows
- • Build foundational knowledge of MLOps practices
- • Participate in ML code reviews and discussions
- • Document model behavior and deployment processes
ML Engineering at L1 — the competency bar
AI in this role
- • Generating ML pipeline code
- • Debugging model issues
- • Writing ML tests
L2 — Junior ML Engineer Rotational
Mission
Deploy and maintain ML models
This tour of duty
Own ML systems that serve real traffic
Own the outcomes
- • Deploy and maintain ML models in production
- • Build training and inference pipelines
- • Implement model monitoring and alerting
- • Collaborate with data scientists on productionization
- • Troubleshoot model performance issues
- • Create ML system documentation
ML Engineering at L2 — the competency bar
AI in this role
- • Drafting ML architectures
- • Analyzing model performance
- • Generating documentation
L3 — Senior ML Engineer Rotational
Mission
Own ML systems with strong MLOps practices
This tour of duty
Lead a project that improves model performance
Own the outcomes
- • Own ML systems for production models
- • Design feature stores and training infrastructure
- • Build model serving systems with strong reliability
- • Partner with DS teams on model requirements
- • Develop reusable ML infrastructure components
- • Mentor junior engineers on MLOps
ML Engineering at L3 — the competency bar
AI in this role
- • Modeling ML systems
- • Reviewing ML code
- • Creating experiment frameworks
L4 — Staff ML Engineer / Manager, ML Engineering Transformational
Mission
Lead ML projects and mentor others
This tour of duty
Design ML infrastructure that teams build on
Own the outcomes
- • Lead ML engineering projects across systems
- • Design scalable ML platform architectures
- • Establish ML reliability and monitoring standards
- • Mentor engineers on ML systems thinking
- • Influence ML technology decisions
- • Build relationships with DS and product teams
ML Engineering at L4 — the competency bar
AI in this role
- • Designing ML platforms
- • Analyzing ML patterns
- • Generating specs
L5 — Senior Staff ML Engineer / Senior Manager, ML Engineering Transformational
Mission
Drive ML architecture across the org
This tour of duty
Drive ML architecture decisions across teams
Own the outcomes
- • Define ML engineering strategy and practices
- • Set standards for ML infrastructure development
- • Lead complex ML platform initiatives
- • Drive MLOps best practice adoption
- • Shape ML engineering practices org-wide
- • Represent ML engineering in technical planning
ML Engineering at L5 — the competency bar
AI in this role
- • Evaluating ML technologies
- • Building MLOps docs
- • Creating roadmaps
L6 — Director, ML Engineering Transformational
Mission
Set ML engineering direction
This tour of duty
Define ML standards that shape practices
Own the outcomes
- • Define ML infrastructure vision and roadmap
- • Architect MLOps standards and platforms that scale
- • Solve the most complex ML engineering and productionization challenges
- • Own ML system reliability and efficiency metrics
- • Drive organizational alignment on ML priorities
- • Represent ML engineering in executive discussions
ML Engineering at L6 — the competency bar
AI in this role
- • Analyzing ML patterns at scale
- • Generating standards
- • Building knowledge bases
L7 — Senior Director, ML Engineering Transformational
Mission
Shape the company's ML vision
This tour of duty
Solve an ML problem that unlocks capabilities
Own the outcomes
- • Set ML engineering strategy company-wide
- • Align ML infrastructure with AI product goals
- • Build hiring and development for ML engineering
- • Define industry-leading ML platform capabilities
- • Shape company-wide ML operating model
- • Lead ML engineering planning and investment
ML Engineering at L7 — the competency bar
AI in this role
- • Modeling ML evolution
- • Analyzing research trends
- • Creating vision documents
L8 — VP, ML Engineering Foundational
Mission
Build and lead ML teams
This tour of duty
Build an ML team that ships reliable models
Own the outcomes
- • Own ML engineering outcomes at scale
- • Drive transformation for ML excellence
- • Build systems for model reliability and efficiency
- • Shape company strategy through ML infrastructure lens
- • Establish operating model for ML organization
- • Lead cross-functional ML alignment
ML Engineering at L8 — the competency bar
AI in this role
- • Building ML dashboards
- • Analyzing team patterns
- • Creating hiring frameworks
L9 — SVP of ML Engineering Foundational
Mission
Own ML strategy and execution
This tour of duty
Transform ML engineering practices
Own the outcomes
- • Shape company ML engineering vision
- • Represent ML engineering at executive level
- • Define multi-year ML infrastructure roadmap
- • Build ML partnerships and industry relationships
- • Lead ML engineering through AI evolution
- • Establish ML engineering as company differentiator
ML Engineering at L9 — the competency bar
AI in this role
- • Modeling ML scenarios
- • Building strategy documents
- • Designing knowledge infrastructure
What Hiring Managers Look For
You ship production ML models that move business metrics, not just experiments in notebooks.
You architect systems that scale beyond the data science team's laptop—distributed training, model serving infrastructure, and MLOps pipelines that survive production chaos.
You transform how the organization thinks about data products, building platforms that democratize ML capabilities across business units while maintaining governance and reliability standards.
Common Career Transitions
ML Engineering → Platform Engineering at L5-L6 for broader infrastructure ownership beyond ML workloads
ML Engineering → Product Data Science at L4-L5 to drive business strategy through advanced analytics
Official Classifications
| System | Code | Official Title |
|---|---|---|
| O*NET-SOC (US) | 15-2051.00 | Data Scientists |
| ISCO-08 (UN/ILO) | 2512 | Software developers |
At L6 and above, the manager classification 1330 — Information and communications technology service managers applies IN ADDITION to the professional code — a manager is a superset of the individual contributor, never a replacement.
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