Staff Machine Learning Engineer - World Foundation Model
- Lead the design, development and benchmarking of state-of-the-art world foundation models for autonomous driving, ranging from data strategy, multistage training, model selection, and eventual deployment and integration with onboard and offboard applications.
- Architect visually realistic simulators to evaluate full end-to-end autonomy stack behavior, from simulating sensors to policy rollouts, across a diverse range of scenario conditions.
- Research and implement cutting-edge approaches across domains (reinforcement learning, probabilistic & generative modeling, scene representations, sensor fusion, temporal reasoning) and validate their effectiveness in simulation and through real-world driving performance.
- Align efforts across various company-internal teams as well as TRI, providing technical mentorship and fostering a collaborative, high-trust engineering culture across organizational boundaries, influencing technical decisions across the partnership, and possibly co-authoring publications for premier conferences and journals.
- Increase the scalability of ML pipelines to support the training and inference of large foundation models, and to optimize edge deployment of state-of-the-art architectures.
- Curate scenarios, develop system introspection capabilities, and establish frameworks for understanding model behavior and performance at scale.
- MS or PhD in computer vision, ML, robotics, or related quantitative fields.
- 7+ years of professional experience with computer vision, ML, or applied science.
- Strong hands-on experience with foundation models, world models, generative AI, multimodal transformers, diffusion, VLAs, or large end-to-end behavior models for robotics or autonomy.
- Expertise in PyTorch (preferred), JAX, or TensorFlow; strong Python and C++ skills.
- Strong understanding of temporal/sequential modeling, probabilistic modeling, reinforcement learning, Bayesian inference, state-space models, and uncertainty quantification.
- Strong understanding of 3D perception, multi-view geometry and sensor fusion.
- Hands-on experience with large-scale distributed training, ML workflows (data curation, training, evaluation, deployment), and inference optimization.
- Knowledge of debugging, profiling and deploying deep neural networks with NVIDIA tooling (CUDA, Nsight, TensorRT) and ONNX.
- Experience with simulation platforms (e.g., CARLA, Applied Intuition, Nvidia DriveSim, etc.), their internal principles and their integration into autonomous system workflows.
- Proven track record of leading large, multi-person technical projects and influencing technical direction across organizations, as well as strong communication skills.
- Publications at top-tier venues (e.g. NeurIPS, CVPR, ICML, ICRA, RSS).
- Experience with closed-loop simulation validation, scenario generation, rare-event or counterfactual testing.
- Experience with multi-agent simulation or high-fidelity 3D environments and game engines (e.g Unreal).
- Experience with 3D generation or reconstruction (e.g., Gaussian Splatting, NeRFs).
- Prior experience in fast-paced R&D environments bridging research and production.
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