Director of Machine Learning Engineering
Directors of ML Engineering manage ML engineering teams and infrastructure. They own ML platform and production systems.
Median Salary
$300,000
Job Growth
High — ML engineering teams increasingly important
Experience Level
Entry to Leadership
Salary Progression
| Experience Level | Annual Salary |
|---|---|
| Entry Level | $200,000 |
| Mid-Level (5-8 years) | $300,000 |
| Senior (8-12 years) | $400,000 |
| Leadership / Principal | $550,000+ |
What Does a Director of Machine Learning Engineering Do?
Directors of ML Engineering lead ML engineering teams and own ML platform and production systems. They manage ML engineers and MLOps specialists, set technical direction for ML systems, establish practices ensuring model quality in production, own ML infrastructure and tooling, and drive adoption of ML across company.
A Typical Day
Team: One-on-ones with ML engineers, career development, performance management
Planning: Set quarterly roadmap for ML infrastructure and platform improvements
Technical: Review ML system designs, architecture decisions
Hiring: Interview and hire ML and MLOps engineers
Stakeholders: Work with data science and product on ML roadmap
Culture: Foster ML engineering excellence and learning
Execution: Remove blockers enabling teams to deliver
Key Skills
Career Progression
Directors of ML Engineering typically progress to VP of Engineering or Chief Technology Officer roles.
How to Get Started
ML engineering: 8+ years as ML engineer or MLOps engineer
Team leadership: Lead projects and small teams
ML systems: Deep experience with production ML systems
Leadership: Team management, mentoring, organizational skills
Communication: Strong executive communication skills
Company: Work at data-intensive or ML-first company
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Frequently Asked Questions
What's the scope?▼
ML platform, infrastructure, deployment systems, model quality, monitoring. ML-specific engineering challenges.
Why separate from general engineering?▼
ML systems have unique challenges (data quality, model quality, retraining, monitoring). Need specialized expertise and tools.
What team typically reports?▼
ML engineers, MLOps engineers, data engineers. Often 20-50 person team.
What's the success metric?▼
Velocity (fast model deployment), quality (accurate models in production), reliability (systems don't break), team quality.
What's the hardest part?▼
Technical complexity, hiring ML engineers, managing pace of research, balancing research and production.
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Last updated: 2026-03-07