Head of AI

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What the market looks like

We've tracked over 1,300+ senior-level AI engineering postings across the US in the last six months, with concentrated hiring in California, New York, and Texas. Strongest candidates bring 8–12 years of hands-on AI/ML engineering experience, a track record of shipping production systems at scale, and the ability to lead and grow technical teams. Compensation for this cohort ranges from $240,000 to $410,000 annually. The market is pulling for leaders who can bridge deep technical credibility with strategy—people who've led platform modernization, built AI/ML infrastructure, or scaled ML ops in resource-constrained environments.

Job responsibilities

• Own the technical vision and roadmap for AI/ML engineering, aligning with business strategy and organizational priorities.

• Lead, mentor, and grow an AI engineering team; set hiring bar, establish best practices, and foster a culture of shipping and iteration.

• Build or evolve AI/ML infrastructure, tooling, and platforms to enable rapid model development, deployment, and monitoring in production.

• Partner with product, data, and business teams to define and deliver AI-powered features and capabilities that drive measurable customer or operational impact.

• Drive decisions on technology stack, model selection, and architecture trade-offs; stay current with AI/ML advancements and evaluate adoption risk and benefit.

• Establish metrics, governance, and quality frameworks for model performance, data integrity, and responsible AI practices.

• Communicate technical progress, wins, and blockers to leadership and cross-functional stakeholders; translate AI complexity for non-technical audiences.

Candidate requirements

• 8–12 years of hands-on AI/ML engineering experience, including 3+ years leading technical teams or owning significant ML platform initiatives.

• Proven track record shipping production AI/ML systems at scale—fluency in model training, inference optimization, MLOps, or ML infrastructure.

• Strong systems thinking: able to architect end-to-end ML pipelines, manage technical debt, and make sound trade-offs between speed and rigor.

• Experience building and scaling high-performing engineering teams; comfort with hiring, feedback, and developing junior engineers.

• Clear communicator who can explain technical decisions to business stakeholders and collaborate across product, data, and ops functions.

• Familiarity with responsible AI, data governance, or compliance frameworks in regulated environments is a plus.