AI Engineering Manager

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What the market looks like

We've tracked over 4,100+ management-level AI engineering postings across the United States in the last six months, with concentrations in California, New York, and Texas. Compensation for this seniority typically ranges from $200,000 to $350,000 annually. The strongest candidates bring hands-on experience shipping AI systems—not just overseeing them—combined with the ability to scale technical teams and translate between deep engineering depth and business outcomes. These leaders excel at balancing rapid iteration with system reliability, mentoring engineers through novel ML challenges, and building processes that accelerate model development without sacrificing code quality.

Job responsibilities

• Lead and grow an AI engineering team—hiring talent, mentoring engineers, and setting technical standards that balance innovation velocity with sustainability

• Own the end-to-end delivery of AI/ML systems from exploration through production deployment, including model training pipelines, inference infrastructure, and monitoring

• Partner with product and data science teams to translate business requirements into technical roadmaps and prioritize work across competing demands

• Drive architecture decisions for AI systems at scale—data pipelines, model serving, experiment tracking, and observability—ensuring solutions are reliable and maintainable

• Build and refine engineering processes for AI development, including code review standards, testing frameworks, and deployment practices tailored to model-driven workflows

• Identify and mitigate technical risks in production AI systems, including model drift, data quality issues, and performance degradation

• Communicate progress, blockers, and technical tradeoffs to cross-functional stakeholders and senior leadership

Candidate requirements

• 5+ years of hands-on AI/ML engineering experience, with at least 2 years in a leadership or senior technical role managing engineers or large-scope projects

• Proven track record shipping production AI systems—model serving, inference optimization, data pipelines, or similar—not just research or prototyping

• Strong foundation in Python and familiarity with modern AI/ML frameworks (PyTorch, TensorFlow, or equivalent) and MLOps tooling

• Experience leading or mentoring technical teams through complex problems and setting quality standards that others follow

• Ability to think strategically about AI engineering challenges while remaining hands-on enough to unblock teams and validate technical approaches