Director, AI Strategy
United States · Remote · Permanent
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What the market looks like
We've tracked 200+ senior-level postings in AI governance across the US in the last six months, with concentrated hiring in California, New York, and Texas. Financial services, professional services, and technology firms are the most active hirers, followed by healthcare, manufacturing, and life sciences. Compensation for this cohort ranges from $210K to $380K base salary. The strongest candidates bring hands-on experience building or scaling AI governance frameworks, translate between technical and non-technical stakeholders with ease, and have navigated real compliance, risk, and ethical dilemmas in production AI environments.
Job responsibilities
Own the design and implementation of enterprise AI governance policies, frameworks, and guardrails that balance innovation velocity with risk management
Lead cross-functional alignment between AI/ML engineering, legal, compliance, risk, and business units to embed governance into the AI development lifecycle
Build and maintain AI risk assessment processes, including model validation, bias detection, and ongoing monitoring protocols
Develop and communicate AI governance standards, documentation requirements, and approval workflows to technical teams and executive leadership
Partner with external stakeholders—regulators, auditors, industry consortia—to stay ahead of emerging regulatory requirements and best practices
Drive adoption of AI governance tools and platforms, integrating them into existing ML ops and data governance infrastructure
Conduct training and change management initiatives to embed responsible AI principles across the organization
Advise the executive team on AI-related regulatory risk, competitive positioning, and strategic governance priorities
Candidate requirements
7+ years in AI governance, risk management, compliance, or AI strategy roles—ideally a mix of technical and policy-facing work
Demonstrated experience building governance frameworks, policies, or control mechanisms in regulated industries or complex organizations
Strong understanding of machine learning workflows and the technical decisions that create governance risk—model development, data pipelines, deployment, monitoring
Track record of influencing cross-functional teams without direct authority—ability to partner with engineering, legal, and business leaders simultaneously
Familiarity with emerging AI regulations (EU AI Act, SEC guidance, industry frameworks) and ability to translate regulatory requirements into operational practice
Excellent communication skills: can explain governance trade-offs to both technical audiences and non-technical executives