Job Description
The role involves developing advanced machine learning models to predict the behavior of surrounding traffic for autonomous driving systems, focusing on creating scalable, realistic, and controllable prediction models to enhance safety and performance in self-driving vehicles.
Key Responsibilities
- Design and build scalable machine learning-based prediction systems for autonomous driving.
- Develop state-of-the-art models for predicting the behavior of surrounding traffic using generative sequence modeling and other advanced techniques.
- Collaborate with the Planning team to create realistic, controllable agents for closed-loop simulation and reinforcement learning.
- Research and implement innovative methods in generative modeling, decision-making, and agent controllability to improve autonomous system performance.
- Mitigate uncertainties across interconnected autonomy components to enhance system reliability and safety.
Requirements
- You have an M.Sc. or Ph.D. preferably focusing on one or more of the following areas: Computer Science, Artificial Intelligence, Mathematics, or a closely related field.
- Subject matter expertise and research experience in one or more of the following areas: sequential decision-making, prediction, Imitation Learning, Deep Reinforcement Learning, generative modeling, large models pretraining finetuning, or machine learning for robotics.
- Strong problem solving and programming skills in Python are required, and C is beneficial.
- Experience with ML frameworks such as PyTorch.
- You have at least 2 years of deploying machine learning systems onboard, ideally in the area of prediction.
- Demonstrated research publications in top conferences such as NeurIPS, ICLR, ICML, CVPR, RSS, CoRL, ICRA, IROS, etc.
- The position requires the ability to design and build scalable, machine learning-based prediction systems to generate multi-modal, realistic, and kinematically feasible trajectories.
- The role involves conducting cutting-edge research in generative sequence modeling and sequential decision-making, including areas such as scalable generative sequence modeling approaches, marginal, conditional, and joint distribution modeling for interactive agents, transformer-based encoder-decoder architectures, large generative models, diffusion models, and controllability of agents via conditioning and guidance techniques.
- Collaboration with the Planning team to design realistic and controllable agents for closed-loop simulation, enabling agent training via Reinforcement Learning (RL).
- Mitigating accumulated uncertainties across interconnected autonomy components.
- Collaborating across various autonomy teams to develop holistic solutions for top challenges, proposing ideas, and prioritizing tasks.
- The ability to derive practical, deployable solutions and see them deployed on real-world vehicles.
Benefits & Perks
Compensation/salary range between $193,930 and $291,150 depending on experience, qualifications, education, location, and skills
Annual performance bonus
Equity
Competitive benefits package
Ready to Apply?
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