Job Description
This role involves developing and deploying advanced machine learning models for autonomous driving, focusing on planning, prediction, and behavior modeling to enhance the safety and efficiency of self-driving vehicles.
Key Responsibilities
- Develop and implement scalable machine learning-based planning and prediction systems for autonomous driving.
- Analyze system performance, data quality, and feature representations to improve autonomy solutions.
- Train, test, and deploy machine learning models using frameworks like PyTorch on real-world vehicles.
Requirements
- Currently pursuing a B.S. or M.S. in Computer Science, Robotics, Mathematics, or a related engineering field.
- Excellent programming proficiency in Python and a strong understanding of object-oriented programming.
- Solid grasp of fundamental computer science concepts, including data structures, algorithms, and software design.
- Experience with C is a plus.
- Strong foundational knowledge of machine learning concepts, e.g., supervised, unsupervised learning, and model evaluation.
- Hands-on experience with at least one major deep learning framework such as PyTorch, TensorFlow, or JAX.
- Demonstrated ability to build projects, shown through coursework, personal projects (e.g., on GitHub), or previous internships.
- Experience or coursework in Deep Learning, Computer Vision, Robotics, or Reinforcement Learning is highly desirable.
- Demonstrated knowledge in ML systems and infrastructure, including training optimization, compiler acceleration, quantization, and efficient deployment of high-performance models, is a strong plus.
- Ability to analyze autonomy system performance, understand data quality and feature representations, and modify machine learning models to develop holistic solutions.
- Experience in implementing and training machine learning models using established frameworks like PyTorch.
- Experience with testing and deploying work on real-world vehicles.
- Ability to conduct experiments, analyze results, and present findings to the team.
- Research experience or focus in areas such as scalable imitation learning, reinforcement learning, generative sequence modeling, sequential decision making, reward modeling, or large models pre-training and fine-tuning.
- For Ph.D. candidates: pursuing a Ph.D. in Computer Science, Artificial Intelligence, Robotics, or a closely related field with a research focus in generative/diffusion modeling, sequential decision making, imitation learning, deep reinforcement learning, or machine learning for robotics.
- A deep understanding of the theoretical underpinnings of modern machine learning.
- Strong programming skills in Python for rapid prototyping and experimentation with complex ML models.
- Experience with C is a plus.
- A track record of research with publications in top-tier conferences such as NeurIPS, ICLR, ICML, CVPR, RSS, or CoRL.
- Demonstrated curiosity, attention to detail, persistence, and the ability to work both independently and collaboratively within a research-oriented team.
- Strong technical communication skills.
Benefits & Perks
Compensation/salary range: between 11,500 and 14,000 per month
Work schedule: Internship duration of 6 months preferred
Work environment perks: Collaborative team environment, opportunity to work on real-world vehicles, inclusive and diverse workplace
Additional benefits: Opportunities for research publication, exposure to cutting-edge AI and machine learning technologies, professional development in robotics and autonomous driving
Ready to Apply?
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