VP, AI Engineering
United States · Remote · Permanent
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What the market looks like
We've tracked 100+ senior-level postings in data science over the last six months, concentrated in technology, professional services, and financial services hubs across California, New York, and Washington. Compensation for this cohort ranges from $190k to $350k annually. The strongest candidates in this market bring proven track records building and scaling high-performing data science teams, shipping production ML systems at scale, and translating technical capability into measurable business outcomes. They combine deep technical credibility with the leadership rigor to hire, mentor, and retain world-class engineers.
Job responsibilities
Lead the end-to-end strategy and execution of AI/ML engineering initiatives, from conception through production deployment and optimization
Build and scale a high-performing data science and ML engineering team, including hiring, mentoring, and career development of senior technical talent
Own the technical architecture and infrastructure decisions that enable rapid experimentation and reliable model deployment at scale
Partner with product, engineering, and business leaders to identify high-impact AI/ML use cases and translate them into prioritized roadmaps
Establish best practices around model governance, testing, monitoring, and performance tracking across the data science organization
Drive adoption of AI/ML capabilities across the business, including communication of capabilities, limitations, and ROI to non-technical stakeholders
Stay current with advances in AI/ML methodologies and emerging tools, and evaluate their relevance to organizational priorities
Candidate requirements
8+ years of experience in data science, machine learning engineering, or AI/ML product development, with at least 3 years in a leadership or senior IC role managing teams or major technical initiatives
Demonstrated ability to ship production ML systems and scale data science organizations from early stage through maturity
Deep technical fluency in machine learning fundamentals, statistical methods, and modern ML frameworks and platforms
Proven track record hiring, developing, and retaining high-caliber data scientists and ML engineers
Strong communication skills with ability to translate technical work into business impact for executives and cross-functional partners
Experience navigating the practical challenges of ML in production (data quality, model drift, latency, cost) and implementing solutions