Senior Research Engineer - Autonomous Vehicles

About the job

Responsibilities

• Develop large-scale supervised and reinforcement learning training frameworks for multi-modal foundation models on thousands of GPUs.
• Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets.
• Implement scalable data loaders and preprocessors for multimodal datasets (videos, text, sensor data).
• Build and optimize simulation infrastructure for large-scale AV driving policy training.
• Collaborate with researchers to integrate cutting-edge model architectures into scalable training pipelines.
• Develop sim-to-real transfer pipelines and deploy to real-world cars.
• Propose scalable solutions combining LLMs with policy learning.
• Apply reinforcement learning to fine-tune multimodal LLMs.
• Develop robust monitoring and debugging tools for training workflows on large GPU clusters.

Requirements

• Bachelor's degree in Computer Science, Robotics, Engineering, or equivalent experience.
• 10+ years of full-time industry experience in large-scale MLOps and AI infrastructure.
• Proven experience designing and optimizing distributed training systems with frameworks like PyTorch, JAX, or TensorFlow.
• Deep familiarity with reinforcement learning algorithms (e.g., PPO, SAC, Q-learning) and hyperparameter tuning.
• Familiarity with policy learning techniques (e.g., reward shaping, domain randomization, curriculum learning).
• Deep understanding of GPU acceleration, CUDA programming, and cluster management tools (e.g., Kubernetes).
• Strong programming skills in Python and C++.
• Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools (e.g., SLURM, Kubernetes).

Benefits

• Equity
• Benefits