ML Engineering Manager

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What the market looks like

We've tracked 100+ management-level ML engineering postings in the United States over the last six months, with the strongest hiring concentrated in California, Washington, and New York across technology, financial services, and professional services. Compensation for this cohort typically ranges from $200K to $340K annually. The most effective managers in this space combine hands-on ML fluency with the ability to scale teams and unblock technical bottlenecks—they speak both the language of model development and organizational systems, and they build cultures where engineers can ship production systems reliably.

Job responsibilities

• Lead and grow an ML engineering team, including hiring, performance management, and career development for individual contributors

• Own the technical roadmap and delivery of ML systems—from model development through production deployment and monitoring

• Partner with product, data science, and infrastructure teams to translate business requirements into ML architecture and implementation plans

• Drive best practices around model evaluation, testing, reproducibility, and operational reliability across the team

• Build and maintain the infrastructure, tooling, and processes that enable the team to experiment quickly and deploy safely

• Communicate technical progress, blockers, and trade-offs to non-technical stakeholders and executive leadership

• Identify and mitigate technical debt, performance bottlenecks, and skill gaps within the organization

Candidate requirements

• 5+ years of hands-on machine learning engineering experience, including building and shipping models to production

• 2+ years in a management, tech lead, or senior IC role with direct experience coaching and growing other engineers

• Strong foundation in ML systems design—familiarity with model training, evaluation, deployment, and monitoring in real-world contexts

• Demonstrated ability to balance technical depth with organizational thinking; you remain credible in technical discussions while thinking about team scaling and process

• Experience working cross-functionally with product, data science, and platform/infrastructure teams to deliver end-to-end solutions

• Clear communication skills, especially the ability to explain technical concepts and trade-offs to non-technical audiences