ML Engineering Manager
Boston, MA · In-person · Permanent
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What the market looks like
We've tracked 65 management-level ML engineering postings across the US in the last six months, with strongest hiring concentration in California, Washington, and New York. The role spans technology, financial services, manufacturing, and healthcare sectors. Compensation for this level typically lands in the $200k–$380k range. The strongest candidates combine hands-on ML expertise with proven ability to scale teams and ship production systems—they lead by example, unblock their engineers, and bridge the gap between research ambitions and operational delivery.
Job responsibilities
Build and lead an ML engineering team from hiring through onboarding, career development, and performance management
Own the technical strategy and roadmap for ML systems—from model development through deployment, monitoring, and retraining pipelines
Partner with product, data, and platform teams to translate business requirements into ML solutions and define success metrics
Drive code quality, testing rigor, and production best practices across the team; establish standards for model governance and experimentation
Identify and remove technical and organizational blockers; advocate for team resources and tooling investments
Mentor engineers on ML fundamentals, system design, and career growth; create space for learning and exploration
Stay current with ML tooling, architecture patterns, and infrastructure trends; evaluate and integrate new technologies thoughtfully
Candidate requirements
7+ years of hands-on ML or AI/ML engineering experience, including production model development, deployment, and monitoring
3+ years managing or leading ML engineers; demonstrated ability to hire, mentor, and retain strong technical talent
Fluency in the full ML lifecycle: data preparation, feature engineering, model training, evaluation, deployment, and operational maintenance
Strong systems thinking—you design for scalability, reliability, and observability, not just model performance
Comfortable working in-person in Boston; ability to build team culture and psychological safety through presence and direct collaboration
Track record of shipping ML systems that drive measurable business value, not proof-of-concepts