Vice President of AI

About the role

We've tracked 200+ senior-level AI engineering postings across the US in the last six months, with strongest hiring concentrated in California, New York, and Texas. These roles span professional services, technology, financial services, and manufacturing—sectors accelerating ML infrastructure and model deployment.

Pay

Compensation for this cohort ranges from $200K to $490K annually.

Responsibilities

• Own the vision and roadmap for AI/ML engineering strategy, aligning technical capability with business objectives and competitive positioning
• Build and scale a high-performing AI engineering organization, including hiring, mentoring, and retaining top technical talent across distributed teams
• Drive the architecture, deployment, and optimization of machine learning models and systems in production environments, ensuring reliability, performance, and cost efficiency
• Partner with product, data science, and infrastructure teams to integrate AI capabilities into core platform offerings and customer solutions
• Lead the evaluation and adoption of new AI technologies, frameworks, and tools—balancing innovation with engineering rigor and operational stability
• Establish AI engineering best practices, standards, and governance across the organization, including model lifecycle management and responsible AI principles
• Communicate technical progress, risks, and opportunities to executive leadership and board-level stakeholders

Requirements

• 8+ years of hands-on AI/ML engineering experience, with at least 3+ years in a senior leadership or architect role overseeing AI/ML systems at scale
• Proven ability to ship production machine learning systems—from model training and optimization through deployment, monitoring, and iteration
• Experience building and managing high-performing engineering teams, with a track record of developing talent and fostering a culture of technical excellence
• Deep fluency in modern ML frameworks, infrastructure, and tooling (PyTorch, TensorFlow, Kubernetes, MLOps platforms, or equivalent production environments)
• Strategic mindset combined with hands-on technical credibility—able to shape long-term AI engineering vision while staying connected to implementation details
• Strong communication skills with the ability to translate technical concepts for non-technical stakeholders and influence across organizational boundaries