Head of Developer Relations & Community – AI

Meta is seeking a senior technical leader to build and scale Developer Relations for our growing portfolio of AI solutions, including Muse API, Muse Code, Muse Spark models and future open-weight releases. This person will define how the global developer community engages with, builds on, and extends Meta's open-source model ecosystem and available tools, driving adoption from individual developers to enterprises to academic research institutions.

You will own the end-to-end developer relationship strategy: growing a vibrant, self-sustaining community around Meta's models; enabling developers to ship great products on top of our technology; and cultivating deep partnerships with academic institutions that advance the state of the art. This role sits at the intersection of engineering, product, and external engagement — and requires someone who can move fluidly between writing code, setting strategy, and delivering compelling presentations to large technical audiences.

Head of Developer Relations & Community – AI Responsibilities:

Define the vision and strategy for developer relations across Meta's closed and open-source AI model portfolio (Muse Spark and future releases), ensuring a coherent developer experience as the portfolio grows

Build and grow a global developer community around Meta's models and solutions from grassroots meetups and hackathons to large-scale programs that drive sustained engagement and contribution

Establish and deepen academic partnerships with leading research institutions worldwide, creating pathways for research collaboration, model adoption in curricula, and joint publications

Be a key external face of Meta's models in front of developers, keynoting at top-tier conferences, engaging directly with influential developers and researchers, and representing Meta in the broader open-source AI ecosystem

Drive developer adoption through technical enablement, reference architectures, sample applications, documentation, tutorials, and tooling that reduce time-to-value for developers building on our models

Own the developer feedback loop, capture signals from the community, distill insights, and route them back to Product and Engineering to shape model releases, APIs, and developer tooling

Define and execute metrics-driven programs to grow active developers, contributors, and community engagementreport regularly on ecosystem health and adoption trajectory

Collaborate cross-functionally with Product, Engineering, Research, Marketing, Partnerships, and Legal to align developer community efforts with Meta's broader open-source strategy

Monitor the competitive and open-source AI landscape, track how developers adopt and build on alternative models (open and closed), and steer Meta's ecosystem strategy to strengthen Meta's ecosystem position

Champion open-source culture internally and externally, advocate for developer-first decisions in model release processes, licensing, and tooling

Minimum Qualifications:

Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
15+ years in developer-facing roles spanning Dev Rel, developer products, engineering, or technical leadership — with significant time spent in open-source or AI/ML ecosystems

Deep technical credibility in AI/ML, large language models, or adjacent areas (model fine-tuning, inference optimization, deployment, evaluation)
Demonstrated ability to build and scale developer communities from early stages to thousands of active contributors

Experience building relationships with academic and research institutions at a strategic level

Proven track record of operating at the executive level with metrics-driven accountability (OKRs, ecosystem KPIs, adoption funnels)
Exceptional public communicator and written communicator with a strong presence in the AI developer community

Experience working cross-functionally across engineering, product, marketing, partnerships, and policy

Preferred Qualifications:

Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ongoing AI skill development…