Director, Machine Learning
United States · Remote · Permanent
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What the market looks like
We've tracked over 100 senior-level ML engineering postings across the US in the last six months, with the strongest concentrations in California, Washington, and New York. Technology, financial services, and manufacturing sectors are actively hiring at this level. Compensation for director-level machine learning leadership typically lands between $240k and $400k annually. The strongest candidates in this cohort bring deep hands-on ML expertise combined with proven ability to scale teams, ship production systems, and translate business problems into technical strategy.
Job responsibilities
Own the technical roadmap and architecture decisions for machine learning systems, balancing innovation with production stability and scalability
Lead and grow an ML engineering team, including hiring, mentorship, career development, and fostering a culture of experimentation and rigorous evaluation
Partner with product, data, and engineering leaders to identify high-impact ML use cases and translate business requirements into technical specifications
Drive end-to-end delivery of ML models from research and prototyping through production deployment, monitoring, and continuous improvement
Build and maintain infrastructure, tooling, and practices that enable the team to move quickly while maintaining model performance and data governance standards
Establish evaluation frameworks and metrics that connect model performance to business outcomes
Represent ML engineering in cross-functional planning, communicating technical constraints, timelines, and opportunities to non-technical stakeholders
Candidate requirements
7+ years of hands-on machine learning engineering experience, with at least 3 years in a leadership or senior technical role managing teams or major systems
Demonstrated ability to take ML models from concept to production, including experience with data pipelines, model training, inference optimization, and monitoring
Track record of building and scaling high-performing teams, including hiring, mentorship, and developing individual contributors
Strong foundation in software engineering fundamentals: version control, testing, CI/CD, and architectural thinking applied to ML systems
Ability to communicate technical complexity to business stakeholders and translate business goals into ML strategy and priorities
Experience navigating organizational and technical trade-offs, and comfort working in ambiguous, fast-moving environments