Director, Software Engineering - Agent Data & Optimization
Meta
- Location
- London, UK
- Work model
- On-Site
- Level
- Staff
Skills
About this role
Meta is seeking a seasoned engineering leader to join our Agent Data & Optimization (ADO) team, which sits right at the heart of Meta's most important AI investments. As part of Meta's Applied AI Engineering organization, ADO teams generate training data and evaluations that directly improve Meta's frontier AI models. We leverage the expertise of our talent to engineer solutions, data, and RL environments that are at the core of AI progress at Meta, fueling the complex capabilities we build, how our models reason, and how they interact with the world.
What is ADO? As part of the new Applied AI org, we generate training data and evaluations that directly improve Meta's frontier AI models — a top company priority. We leverage the expertise of our talent to engineer solutions, data, and RL environments that help train our models. This unique data is a key differentiating factor for Meta, complementing our compute investments and great model architecture. Our work is already shipping in model releases like Muse Spark and moving benchmarks.
Why this is exciting: You get to work directly on frontier AI models — not adjacent to them. We're essentially the largest, best-funded AI startup ever: 4 months old, already delivering real impact, extremely flexible, and highly entrepreneurial. You can actually point to results — the work from our team went directly into the latest Spark release, and our coding benchmarks are closing the gap on leading competitors. You’d lead a sizable, flat engineering organization across multiple domains, partner directly with Meta's model training teams (MSL), and help shape the culture of something genuinely new.
You'll influence technical decision making and organizational strategy, driving prioritization and execution while managing outstanding engineers and engineering managers in a fast-paced, entrepreneurial environment.
Who we're looking for: Generalist engineering leaders who manage at scale, are AI-native in mindset, and thrive in ambiguity. Breadth over deep specialization. Builders who have a strong technical foundation, advocate for their people, and have a proven track record of structuring healthy organizations that foster collaboration across multiple disciplines. Highly energized by shaping something from the ground up.