Director of machine learning job description.

Hiring a director of machine learning or preparing for senior leadership in AI? This job description outlines responsibilities including team development, research oversight, business strategy alignment, and innovation planning — plus what to expect in terms of salary at director level.

Table of contents

    What does a director of machine learning do?

     

    The director of machine learning leads teams developing and deploying machine learning across business-critical functions. They define vision, set technical direction, manage cross-functional collaboration, and ensure ML work supports broader strategy.

     

    Key responsibilities include overseeing experimentation, managing roadmaps, building team capability, and communicating with senior stakeholders. They align ML initiatives with KPIs like revenue growth, customer retention, or operational efficiency.

     

    In scaling businesses, they help institutionalise ML practices. In global organisations, they lead distributed teams, manage research partnerships, and contribute to data and AI governance at the highest level.

     

    Key responsibilities of a director of machine learning.

     

    The director of machine learning leads the strategic deployment of ML across products and teams. Responsibilities typically include:

    • Leading ML teams across research, engineering, and infrastructure

    • Aligning ML goals with product strategy and commercial outcomes

    • Managing platform scale, model portfolio, and performance monitoring

    • Ensuring model governance, security, and responsible AI practices

    • Recruiting and mentoring technical ML leaders and engineers

    • Reporting on ML performance, ROI, and roadmap to executives

    • Collaborating with product and data teams on integration and experimentation

    • Overseeing tooling, vendor relationships, and infrastructure investment

    • Driving innovation through collaboration with academia or startups

    • Supporting strategic partnerships and external engagements on AI/ML

    This role blends strategic leadership, delivery oversight, and technical excellence.

     

    Skills and requirements for a director of machine learning.

     

    Directors of machine learning lead AI strategy, delivery, and innovation. Employers typically look for:

    • 10+ years of experience in AI, machine learning, or data science leadership

    • Experience managing cross-functional ML and research teams

    • Proven success delivering real-world AI at scale

    • Skilled in managing roadmaps, metrics, and priorities

    • Deep understanding of production-grade ML and model reliability

    • Familiarity with ethical, regulatory, and responsible AI practices

    • Ability to align technical work with commercial strategy

    • Strong stakeholder management and executive reporting skills

    • Experience building hiring plans and team culture

    Most Directors of ML report to CTOs or CDOs, shaping applied machine learning across products.

     

    Average salary for a director of machine learning.

     

    In the UK, the average salary for a director of machine learning typically ranges from £90,000 to £130,000, depending on team leadership, research-to-product execution, and commercial impact.

    • Mid-level directors tend to earn between £90,000 and £110,000

    • Senior directors leading large ML teams or responsible for innovation pipelines can earn between £111,000 and £130,000

    • Bonus schemes or equity are common in AI-first or high-growth businesses

    Top-paying opportunities exist in enterprise AI, autonomous systems, and deep tech startups.

     

    Career progression for a director of machine learning.

     

    A director of machine learning oversees the development and deployment of machine learning systems at scale. This executive-level role drives business value through AI strategy and technical leadership. A typical career progression includes:

     

    ML engineer / Data scientist

     

    Delivers core models and experiments. Collaborates with cross-functional teams.

     

    Senior ML engineer / ML lead

     

    Owns delivery of key projects, manages engineers, and sets performance metrics.

     

    Director of machine learning

     

    Leads a department of engineers and scientists. Aligns modelling work with strategic business goals.

     

    VP of AI / Head of data science

     

    Oversees the integration of AI into product strategy. Defines governance, resourcing, and future capability.

     

    CTO

     

    Transitions into broader technical leadership with a focus on transformation and innovation.

    MEET THE TEAM

    Meet our team of data recruiters.

    Harry Griffiths
    Harry Griffiths

    Co-Founder

    Luke Rose
    Luke Rose

    Development - Europe

    Zak Jones
    Zak Jones

    DevOps, Cloud & Infrastructure - UK

    salary guide

    Our UK data salary guide.

    Directors of machine learning lead strategy, delivery, and innovation across ML teams. Salary should reflect leadership across research, engineering, and platform scalability.

     

    Our UK data salary guide provides benchmarks for director-level ML roles, 2024 comparisons, hiring insight, and projections through to 2026.

    FAQS

    Director of machine learning FAQs.

    They lead teams of ML engineers, data scientists, and MLOps professionals — setting vision, ensuring model ROI, and integrating ML into the product roadmap. They also handle hiring, budget, tooling strategy, and executive reporting.

    A head of data science may focus more on experimentation and stakeholder alignment. The director of ML is often more delivery-focused — making sure ML is reliable, scalable, and tied to outcomes.

    Models that are adopted, not just developed. Success is measured by product impact, stability in production, model governance, and cross-functional trust — not just accuracy.

    Those who can switch from architecture to hiring to strategy in a single day — and who’ve scaled teams across experimentation and ops. Real-world shipping experience and platform thinking are critical.

    VP of AI, chief AI officer, or CTO — particularly in AI-native businesses. Others move into advisory, investment, or founding roles in applied ML ventures.

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