Head of AI

Axial Search; United States; On-site; USD 240,000 - 410,000; Full time

Job summary

Axial Search is seeking an senior AI/ML engineering leader in the United States to own the technical vision and roadmap for AI/ML initiatives. You will mentor a growing team, shape ML infrastructure, and partner with product and data teams to deliver AI-powered features with measurable impact.

The role requires 8-12 years of hands-on experience and a proven track record of shipping production AI/ML systems at scale, including MLOps, inference optimization, and platform modernization.

Qualifications

• 8-12 years of hands-on AI/ML engineering experience; 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; architect end-to-end ML pipelines and manage technical debt.

Responsibilities

• Own the technical vision and roadmap for AI/ML engineering; align with strategy.
• Lead, mentor and grow an AI engineering team; establish best practices.
• Build or evolve AI/ML infrastructure, tooling, and platforms for rapid development and production.
• Partner with product, data, and business teams to deliver AI-powered features with measurable impact.
• Drive decisions on technology stack and architecture; evaluate AI advancements.
• Establish metrics, governance, and quality frameworks for model performance and data integrity.
• Communicate progress to leadership and translate AI concepts for non-technical audiences.

Skills

8-12 years AI/ML experience

3+ years leading teams

ML model development

ML infrastructure

Communication with stakeholders

Job description

Axial Search is a specialist executive search firm built for one kind of hire: leaders who help organizations navigate AI transformation.

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.