Technical Lead Manager, Synthetic Data

Job description

Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. 🛠️ About our Simulation Teams Simulation is advancing our end-to-end autonomous driving research. The team’s mission is to accelerate our journey to AV2.0 by incubating capabilities that become company-level advantages. GAIA, our generative world models, and the synthetic data they produce, are one of those. This role leads Synthetic Data within Simulation. The team exists to dramatically reduce our dependency on expensive and time-consuming on-road data collection by turning generative world models (models like GAIA-3) into a production engine for training-grade experience. When we can restage real driving onto a camera rig that does not exist yet, rewrite ego motion to create scenarios we have never encountered, and land that data in the same training stack we use for real driving, we can train, evaluate and deploy on vehicles and in geographies we have barely collected from. As Tech Lead Manager, you’ll lead a high-performing team of machine learning engineers and help us answer questions like: can we train and validate a driving model for a vehicle platform before the fleet exists, can synthetically generated data replace scarce real-world data for training and evaluation, and how quickly can we deploy autonomous driving in a geography where we’ve never collected AV data? 🧠 Your day-to-day - Algorithmic and system design: define approach to GAIA generation, system design of the synthetic data pipeline and controllability engine, focus on efficiency and quality. - Cross-team collaboration: aligning with driving-model owners and with evaluation and safety team on utility and scalability of generated data. - Mentorship / async review: reviewing a teammate’s experimental design, PR, or ablation write-up and giving direct feedback. - 1:1s and feedback: building rapport and managing your direct reports. - Up to date with state of the art: keeping current on video generation, camera transfer, distillation — insofar as it changes a checkpoint or a mix we ship. - Strategy: leading or joining strategic discussions on the synthetic data roadmap, safety case and platform bring-up sequencing. 🧩 What you’ll be working on - Architect the future — set the technical direction for how we post-train and condition world models for synthetic-data capabilities (rig transfer, pose transfer, controllability), holding a high bar for what counts as training-grade generation. - Own the loop end to end — make sure generation, evaluation and training stay one system: from checkpoint and config, through large-scale GPU inference, to artefacts that land in driving-model training with reproducible lineage. - Get hands-on when it matters — lead from the front on key components, codebases and experiments. - Push throughput and yield — drive inference optimisation (distillation, few-step sampling, KV caching, step count), valid-generation rate, and self-serve workflows so model developers can request synthetic sets without a specialist in the loop. - Disrupt thoughtfully — challenge assumptions about where synthetic data pays off, ask sharp questions, and champion bold ideas that move us beyond incremental gains. - Make things happen — lead a high-performing, cross-functional team of ML engineers and applied scientists working across generative modelling, generation infrastructure and training. Drive quarterly planning and execution in a high-ambiguity environment where the target moves. - Align and connect — collaborate with world-model researchers, platform and infra engineers, driving-model owners and evaluation so synthetic data is integrated into the broader stack, not delivered over a wall. Manage upwards and laterally to align your team’s goals with company priorities and OEM programme timelines. - Architect teams — grow and structure a resilient team by hiring top talent, designing effective operating models, and fostering a sense of belonging regardless of location. Cultivate a strong, inclusive culture rooted in scientific rigour, collaboration and curiosity. - Level up — coach and mentor team members, tailoring growth plans to individual strengths and aspirations. Lead by example through technical engagement and clear feedback. - Champion change — navigate your team through evolving research priorities and fast-moving execution, maintaining stability and trust through uncertainty. 🙌 You should apply if - 5+ years of experience in ML engineering or applied research roles, with a track record of training and shipping neural networks — not only operating data platforms. - 4+ years of people management experience, including direct reports and cross-function