AI Engineering Manager

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What the market looks like

We've tracked 75 management-level ML engineering postings in the US over the last six months, with strongest concentration in California, Washington, and New York. The role spans technology, financial services, manufacturing, and healthcare sectors. Compensation typically ranges from $200K to $340K annually. The strongest candidates bring hands-on ML infrastructure experience alongside proven team leadership—they've shipped production systems, scaled engineering teams through growth phases, and can translate between data science ambitions and engineering constraints.

Job responsibilities

• Lead and grow an ML engineering team, owning hiring, mentorship, and career development while maintaining technical credibility through hands-on contribution

• Own the design and delivery of ML infrastructure, pipelines, and systems—from model training workflows to production deployment and monitoring

• Drive technical roadmap decisions: evaluate build vs. buy for ML tools, manage tech debt, and align engineering efforts with business outcomes

• Partner with data scientists and product teams to translate model research into robust, scalable production systems

• Establish ML engineering best practices—reproducibility, versioning, testing, and observability—across the org

• Own incident response and system reliability for production ML workloads, including monitoring, debugging, and post-mortems

• Build and communicate quarterly roadmaps, track progress against KPIs, and articulate ML engineering's impact to leadership

Candidate requirements

• 7+ years of ML engineering or infrastructure experience, with at least 2 years in a management or technical leadership role

• Proven track record shipping and operating production ML systems at scale—model deployment, feature stores, retraining pipelines, or similar core infrastructure

• Strong foundation in ML fundamentals (training, evaluation, validation) and proficiency in Python or similar languages; hands-on depth with ML frameworks (PyTorch, TensorFlow, scikit-learn)

• Demonstrated ability to lead, mentor, and grow engineering teams through ambiguity; comfort with hiring and performance management

• Clear communicator who can distill technical complexity for non-technical stakeholders and negotiate trade-offs between rigor and speed

• Familiarity with cloud ML platforms (AWS SageMaker, Google Vertex, Azure ML) or strong containerization and orchestration knowledge (Docker, Kubernetes)