Director, Machine Learning

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What the market looks like

We've tracked 100+ senior-level machine learning engineering postings across the US in the last six months, concentrated in California, Washington, and New York. The strongest candidates bring deep experience shipping production ML systems at scale—not just prototyping—combined with the ability to lead and scale ML teams through technical rigor and clear communication. These roles span technology, financial services, healthcare, and manufacturing, with compensation typically landing between $240K and $400K. Directors in this space own both the technical direction of ML initiatives and the people and processes that make them sustainable.

Job responsibilities

• Own the technical strategy and roadmap for machine learning engineering, including model development, deployment, monitoring, and continuous improvement of production systems.

• Lead and grow a machine learning engineering team—hiring, mentoring, and creating a culture of technical excellence and ownership.

• Partner with product, data science, and engineering leadership to translate business requirements into ML initiatives and ship models to production on time.

• Drive infrastructure and tooling decisions—MLOps platforms, data pipelines, model serving, experiment tracking—that enable the team to move fast without sacrificing reliability.

• Establish best practices for model evaluation, testing, and monitoring in production; own the quality bar for deployed systems.

• Collaborate with cross-functional stakeholders to scope feasibility, manage technical risk, and communicate progress and trade-offs clearly to non-technical audiences.

• Champion organizational learning: build documentation, conduct design reviews, and foster knowledge-sharing across the ML and broader engineering organization.

Candidate requirements

• 7+ years of experience in machine learning engineering, including 3+ years leading teams or owning the technical direction of production ML systems at scale.

• Deep hands-on fluency: you've built, trained, deployed, and monitored ML models in production and understand the operational challenges and trade-offs that come with it.

• Demonstrated ability to lead and develop engineers—building team culture, conducting technical interviews, mentoring individual contributors, and scaling capability over time.

• Strong grasp of ML fundamentals (model selection, evaluation metrics, overfitting, feature engineering) and infrastructure concerns (data pipelines, feature stores, model serving, experiment tracking).

• Excellent communicator: you can distill complex technical concepts for non-technical stakeholders and write clear technical specifications, design docs, and postmortems.

• Track record shipping production systems on time—you understand scope, estimation, and how to unblock teams when things get stuck.