Manager, ML Engineering

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What the market looks like

We've tracked 75 management-level ML Engineering postings in the United States over the last six months, with concentrations in California, Washington, and New York—particularly in San Francisco, Seattle, and New York City. These roles span technology, financial services, healthcare, and manufacturing, reflecting broad AI adoption across sectors. Compensation for this cohort ranges from $200,000 to $340,000 annually. The strongest candidates bring hands-on experience shipping production ML systems, a track record of building and scaling engineering teams, and the ability to bridge technical depth with organizational strategy—balancing model performance with deployment realities and business impact.

Job responsibilities

• Lead and grow an ML engineering team, including hiring, mentoring, and performance development while fostering a culture of technical excellence and rapid iteration

• Own the ML system architecture and engineering roadmap, ensuring models move from experimentation to production with reliability, scalability, and maintainability

• Partner with data science, platform engineering, and product teams to translate business requirements into ML solutions and define success metrics

• Drive best practices around model training, validation, deployment pipelines, monitoring, and incident response across the organization

• Evaluate and integrate ML infrastructure, tools, and frameworks—from feature stores to model serving—that enable the team to ship faster and operate at scale

• Collaborate with leadership to prioritize initiatives, manage trade-offs between technical debt and velocity, and align ML investments with business outcomes

• Champion code quality, testing discipline, and documentation standards to ensure team work is maintainable and reproducible

Candidate requirements

• 5+ years of ML engineering experience, including at least 2 years in a management or technical leadership role, with a portfolio of production ML systems you've shipped

• Deep hands-on fluency in ML engineering fundamentals: data pipeline design, model training and evaluation, deployment and serving infrastructure, monitoring and retraining

• Proven ability to build and develop engineering teams, hire strong talent, and create psychological safety for technical risk-taking and learning

• Experience working cross-functionally with data scientists, product managers, and infrastructure engineers to unblock problems and deliver end-to-end solutions

• Strong communication skills—ability to explain technical tradeoffs and ML limitations to non-technical stakeholders and translate business constraints into engineering decisions

• Comfort with ambiguity in early-stage ML work; experience managing projects where the path forward isn't yet clear