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NuroNuroMountain View, CA

ML Research Scientist, Prediction & Smart Agents

Build state-of-the-art ML models to predict traffic behavior for autonomous driving, using generative sequence modeling and controllable agents for planning and simulation. Requires PhD preferred, 2+ years deploying ML systems, and expertise in PyTorch and robotics ML.

194k – 291k
On-site2+ YOEAI Research

About the role

About the Work

  • Design and build scalable, machine learning-based prediction systems to generate multi-modal, realistic, and kinematically feasible trajectories.
  • Conduct cutting-edge research in generative sequence modeling and sequential decision-making, including:
    • Scalable generative sequence modeling approaches.
    • Marginal, conditional, and joint distribution modeling for interactive agents.
    • Transformer-based encoder-decoder architectures.
    • Large generative models and diffusion models.
    • Controllability of agents via conditioning, guidance, and other techniques.
  • Collaborate closely with the Planning team to design realistic and controllable agents for closed-loop simulation, enabling agent training via Reinforcement Learning (RL).
  • Mitigate accumulated uncertainties across interconnected autonomy components.
  • Collaborate across various autonomy teams to develop holistic solutions for top challenges, proposing ideas, prioritizing.
  • Derive practical, deployable solutions and see them deployed on real-world vehicles.

About You

  • Education: M.Sc. or Ph.D. (preferable) in Computer Science, Artificial Intelligence, Mathematics, or related field.
  • Expertise: Research experience in sequential decision-making, prediction, Imitation Learning, Deep Reinforcement Learning, generative modeling, large models, or ML for robotics.
  • Technical Skills: Strong problem solving and programming in Python (required), C++ (beneficial), and ML frameworks like PyTorch.
  • Experience: 2+ years deploying ML systems onboard, ideally in prediction.
  • Publications: Demonstrated research in top conferences (NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA, IROS).

Nice to have: Deep background in Embodied AI for robotics, Causal reasoning, Model interpretability, Joint prediction and planning, Diffusion Models.

Compensation: Base pay range $193,930 - $291,150, plus annual performance bonus, equity, and benefits.

Skills

PyTorchPythonC++TransformerGenerative ModelsDiffusion ModelsReinforcement LearningImitation LearningSequential Decision-MakingMachine Learning

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