VP, AI Engineering

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What The Market Looks Like
We've tracked 200+ senior-level AI engineering postings in the US over the last six months, with hiring concentrated in California, New York, and Texas across professional services, technology, financial services, and manufacturing. Compensation for this cohort typically ranges from $200K to $490K base salary. The strongest candidates in this space bring hands-on experience shipping production ML systems, a track record of scaling engineering teams through periods of rapid AI adoption, and the ability to translate between research-grade AI work and operational delivery—combining deep technical credibility with organizational leadership at the VP or director level.
Job Responsibilities
Own the technical strategy and roadmap for AI/ML engineering, translating business priorities into product and platform capabilities that scale across the organization
Build and lead a high-performing AI engineering team—hiring talent, setting technical standards, and creating an environment where engineers ship reliable systems in production
Drive the architecture and implementation of ML platforms, data pipelines, and inference infrastructure that support the organization's AI initiatives
Partner with product, research, and data science teams to integrate AI/ML models into customer-facing and internal applications, owning quality, latency, and reliability
Establish MLOps practices, monitoring, and governance frameworks that ensure models perform in production and meet compliance requirements
Represent engineering in cross-functional AI transformation discussions, advising leadership on technical feasibility, resource trade-offs, and capability gaps
Evaluate and integrate third-party AI tools, platforms, and vendor relationships that accelerate time-to-value for the engineering organization
Candidate Requirements
10+ years of software engineering experience, with at least 5 years building, shipping, and operating machine learning systems in production environments
Demonstrated experience leading and scaling engineering teams (typically 5–30+ engineers) through periods of rapid growth and technical change
Deep hands-on expertise in ML infrastructure, model deployment, experiment tracking, or MLOps—comfortable discussing tradeoffs in training pipelines, inference latency, and model governance
Track record of translating ambiguous AI/ML requirements into concrete engineering roadmaps and shipping systems that deliver measurable business value
Experience working cross-functionally with data science, product, and business teams to align technical decisions with organizational goals
Comfortable in a remote, distributed environment and skilled at building alignment asynchronously across geographies and functions

Work arrangement
Yes