Manager, ML Engineering

Responsibilities

• Lead and grow an ML engineering team, owning hiring, mentoring, and career development while maintaining hands-on technical credibility
• Drive the design and deployment of ML systems from research to production, including model training pipelines, inference infrastructure, and monitoring
• Partner with data science, product, and platform engineering teams to translate business requirements into technical roadmaps and prioritized deliverables
• Own code quality, testing standards, and technical debt management across the team's scope
• Establish best practices for model governance, reproducibility, and performance tracking in production environments
• Advocate for team needs and budget, communicating technical constraints and opportunities to leadership in business terms
• Contribute to company-wide ML strategy and engineering standards, not just team execution

Requirements

• 5+ years of hands-on ML engineering experience, including at least 2 years shipping production ML systems (models, pipelines, inference services, or similar)
• 2+ years in a people leadership or technical mentorship role, demonstrating ability to recruit and develop engineers
• Fluency in ML tooling and infrastructure—model training frameworks, experiment tracking, feature stores, deployment platforms, and monitoring—relevant to your domain
• Demonstrated ability to balance technical depth with business priorities, translating between engineering and non-technical stakeholders
• Experience building and maintaining production systems at scale; comfort with data pipelines, testing, and observability

Pay

Compensation for this cohort ranges from $200k to $340k annually. Based on tracking of 76 management-level ML engineering postings in the US over the last six months, opportunities are concentrated in California, Washington, and New York across technology, financial services, and professional services sectors.