ML Engineering Manager

Axial Search is a specialist executive search firm focused on leadership roles that help organizations navigate AI transformation.

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 these roles ranges from $200k to $350k annually. Successful managers typically 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.

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

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