AI Engineering Manager

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What The Market Looks Like
We've tracked 2,700+ management-level postings in AI architecture across the US over the last six months, with the strongest concentrations in California, Texas, and New York. Compensation for this cohort typically lands between $200K and $390K, with variation based on domain depth and team scope. The most competitive candidates bring hands-on experience shipping AI systems at scale—not just architecture in theory—combined with the ability to lead engineering teams through complex technical decisions and cross-functional collaboration. Organizations are increasingly looking for managers who can bridge the gap between research-grade AI work and production constraints, while mentoring engineers through rapid technology evolution.
Job Responsibilities
Own the technical architecture and design direction for AI/ML systems, from model training pipelines through inference infrastructure and deployment patterns
Lead and grow an AI engineering team, including hiring, mentoring, performance feedback, and creating clear technical growth paths
Drive system design reviews and technical decision-making across model selection, infrastructure choices, data pipelines, and production reliability
Partner with product, data science, and infrastructure teams to translate business requirements into scalable AI system designs
Build and maintain standards for model evaluation, testing, monitoring, and observability across deployed systems
Ship AI features and systems to production, managing tradeoffs between model performance, latency, cost, and operational complexity
Communicate technical strategy and progress to non-technical stakeholders; identify and surface AI-related risks and opportunities to leadership
Candidate Requirements
7+ years of software engineering experience with at least 3 years in AI/ML engineering roles, including hands-on work on production AI systems
2+ years in a leadership or senior IC role managing or mentoring engineering teams, with demonstrated ability to grow and develop people
Deep technical fluency in AI/ML infrastructure: experience with training frameworks, model serving, data pipelines, and production deployment patterns
Proven track record shipping AI systems end-to-end and making architecture decisions that balance model performance, cost, and operational complexity
Strong communication skills and ability to lead cross-functional work with product, data science, and infrastructure partners
Comfort with ambiguity in a fast-moving AI landscape; intellectual curiosity and commitment to continuous learning as AI tooling and best practices evolve

Work arrangement
Yes