ML Engineering Manager
United States · Remote · Permanent
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What the market looks like
We've tracked 67 management-level ML engineering postings across the US in the last six months, with the strongest demand in California, Washington, and New York. Technology, IT services, and manufacturing sectors are hiring most actively. Compensation for management-level ML engineering roles in this cohort ranges from $200k to $350k annually. The managers we see succeed in these roles bring hands-on ML systems experience, a track record of building and scaling teams, and the ability to translate between research-oriented engineers and product-focused stakeholders.
Job responsibilities
Build, mentor, and scale an ML engineering team — hire strong individual contributors, develop career paths, and foster a culture of ownership and technical rigor
Own the end-to-end ML platform roadmap: define priorities, manage trade-offs between research, production stability, and business impact, and drive quarterly planning
Lead the design and delivery of production ML systems — from model architecture and training pipelines to inference infrastructure and monitoring
Partner with product, data science, and infrastructure teams to translate business requirements into technical ML solutions and shipping timelines
Drive quality and velocity through code review, testing standards, documentation, and continuous improvement of the ML development lifecycle
Identify and reduce technical debt, bottlenecks in the ML pipeline, and gaps in tooling or infrastructure that slow the team down
Represent ML engineering in cross-functional planning; advocate for the team's needs and ensure alignment on priorities with leadership
Candidate requirements
5+ years of hands-on ML engineering experience, including designing and shipping production systems (model serving, retraining pipelines, monitoring, A/B testing)
2+ years of people management experience: hiring, feedback, career development, and building psychological safety on technical teams
Deep fluency in Python, MLOps fundamentals (experiment tracking, CI/CD, containerization), and the tradeoffs between model performance and production constraints
Proven ability to communicate technical decisions to non-technical stakeholders and collaborate effectively across engineering, product, and data science
Track record of taking ownership: defining scope, shipping measurable outcomes, and unblocking teams when progress stalls