Machine Learning Engineer, Synthetic Data

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 end-to-end autonomous driving research. The team’s mission is to accelerate AV2.0 by incubating capabilities that become company-level advantages — generative world models and the synthetic data they produce are one of those. The goal of this role is to build, scale, and optimise next-generation world model architectures (GAIA and successors) and bridge them into high-throughput generation and training infrastructure, so synthetic data can dramatically accelerate autonomy development. You will post-train world models for new embodiments and behaviours (rig transfer, pose transfer, dashcam restaging), generate multimodal synthetic experience at scale, and land that data in the same training stack we use for real driving. You sit between ML research and engineering: collaborating with scientists on architecture and conditioning, and with platform engineers on generation jobs, training artefacts, and how synthetic data is mixed into training. Your work will decide how fast we can train, evaluate, and deploy driving models on vehicles we have barely collected from.   🧠 Your day-to-day - Model development: Post-train, distill, and evaluate GAIA-family large video diffusion models to improve generation quality, controllability, and generalisation. Run ablation studies and investigate failures in visual quality, temporal consistency, motion, and action conditioning. - Generation at scale: Build and run synthetic video generation pipelines across tens of thousands of driving segments. Optimise inference efficiency, manage GPU resources, and ensure data quality throughout the pipeline. - From generation to training: Integrate synthetic data across different stages of driving-model training, develop sampling strategies, and measure its impact through evaluation suites and on-road performance. - Cross-team collaboration: Work with driving-model teams to determine effective data mixtures and with simulation and evaluation teams to turn generated scenarios into useful closed-loop tests. - Applied research: Translate advances in world modelling, video generation, flow matching, distillation, and data-centric training into measurable improvements in the models and data we deploy.   🧩 What you’ll be working on - Post-train and iterate GAIA-class world models for synthetic-data capabilities: embodiment transfer (new camera/vehicle embodiments), pose transfer (rewritten ego trajectories), and related conditioning (geometry, calibration, actions). - Own the generation loop: config → large-scale GPU inference → training-ready artefacts, with clear lineage from the model and settings that produced them. - Land synthetic data in driving-model training (behaviour cloning, reward models, RL): binarisation, mix ratios, quality filters, and experiments that measure suite and on-road impact — including when synthetic should replace scarce real rig data.Diagnose and fix geometry, artefacts, calibration, and controllability failures that determine whether generated video is training-grade. - Improve throughput and yield: inference optimisations (distillation, KV cache), valid-generation rate, and self-serve workflows so model developers can request synthetic sets without a specialist. - Expand coverage to new vehicle platforms and safety-critical scenarios (OEM bring-up; Emergency Lane Keeping / Automatic Emergency Braking). - Partner with world-model researchers, infra, and driving-model owners so generation, evaluation, and training stay one system.   🙌 You should apply if - 4+ years in applied ML / research engineering, with a track record