Axial Search recruits for AI transformation roles across North America, building long-term relationships with leaders across every industry vertical. Apply today to express your interest in this position and others like it. Visit our website to learn more about our process and to find free tools to support your job search — including our live job market dashboard with salary, skills, and hiring-trend data from thousands of AI transformation roles.

What the market looks like

We've tracked 2,400+ senior-level AI/ML engineering postings across the US in the last six months, with concentrated hiring in California, New York, and Texas. Compensation for senior-level roles in this function ranges widely—median offers around $220,000, with top-tier packages reaching $410,000. The strongest candidates bring deep hands-on experience shipping ML systems at scale, comfort navigating ambiguous technical strategy decisions, and proven ability to recruit and develop strong engineering teams. They balance hands-on technical depth with the leadership scope required to unblock roadmaps and set direction for multi-year AI initiatives.

Job responsibilities

Own the technical strategy and roadmap for AI/ML initiatives, setting priorities that align with business outcomes and company architecture constraints.
Build and lead a high-performing AI/ML engineering team, including hiring, mentoring, and creating a culture of rigor around model quality, data pipelines, and production reliability.
Partner with product, data science, and platform teams to translate business requirements into scalable ML systems and drive adoption across the organization.
Ship production AI/ML systems end-to-end—from problem definition and data strategy through deployment, monitoring, and continuous improvement.
Establish engineering standards, MLOps practices, and governance frameworks that ensure models stay performant, auditable, and maintainable over time.
Communicate technical progress and blockers to non-technical stakeholders, securing resources and alignment on trade-offs between speed and robustness.
Evaluate and integrate emerging ML tools, frameworks, and infrastructure—making deliberate choices on build versus buy for core platform components.

Candidate requirements

10+ years of software engineering experience, with at least 5 years building, shipping, or leading AI/ML systems in production environments.
Demonstrated experience managing and growing engineering teams of 3+ people, with a track record of hiring talent and developing them into strong individual contributors or leaders.
Strong foundational understanding of machine learning workflows—data pipelines, feature engineering, model training, evaluation, and deployment—and how engineering decisions impact model performance.
Hands-on technical fluency across the ML stack: you've written code for model training, worked with common frameworks (PyTorch, TensorFlow, scikit-learn), and debugged data and model quality issues.
Experience setting technical strategy, making architectural trade-offs, and translating business needs into engineering roadmaps at a team or organizational level.
Comfort communicating with both technical and non-technical audiences—explaining what's possible, what's risky, and what's worth building.

VP, AI Engineering