Director, Machine Learning

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What the market looks like

We've tracked 100+ senior-level ML Engineering postings across the US in the last six months, with strongest concentrations in California, Washington, and New York. Compensation for directors in this cohort typically ranges from $240K to $400K annually. The strongest candidates bring deep experience shipping production ML systems at scale, a track record of building and mentoring high-performing engineering teams, and the ability to translate between technical depth and business outcomes. Organizations are increasingly hiring for this seniority to own the full lifecycle of ML initiatives—from strategy and architecture through deployment and monitoring—rather than treating ML as a support function.

Job responsibilities

• Own the ML engineering roadmap and strategy, translating business objectives into technical priorities and timelines

• Build, mentor, and lead an ML engineering team—from hiring and capability development through performance management and career growth

• Design and oversee ML architecture and best practices across the organization, including model development, deployment pipelines, and monitoring systems

• Partner with product, data, and infrastructure teams to integrate ML capabilities into production systems and ensure reliability at scale

• Drive continuous improvement in ML engineering processes, tooling, and infrastructure to accelerate time-to-value and reduce technical debt

• Communicate ML capabilities, limitations, and outcomes to stakeholders and executive leadership

• Establish and maintain standards for model governance, evaluation, and responsible AI practices

Candidate requirements

• 8+ years of professional machine learning, data science, or AI engineering experience, with at least 3 years in a leadership or staff/principal engineer role

• Demonstrated success building and shipping production ML systems that generated measurable business impact

• Experience leading and growing engineering teams, setting technical direction, and building ML culture and capability

• Strong fundamentals in ML engineering: model training, evaluation, feature engineering, model deployment, and monitoring in production environments

• Ability to work across technical and non-technical stakeholders, translating between deep technical work and business strategy

• Familiarity with modern ML tooling, infrastructure, and DevOps practices (e.g., MLOps pipelines, containerization, cloud platforms)