Senior Research Engineer, Autonomous Vehicles

What you'll do

• Develop large-scale supervised and reinforcement learning training frameworks for multi-modal autonomous vehicle foundation models.
• Optimize GPU and cluster utilization for efficient model training and fine-tuning on massive datasets.
• Implement scalable data loaders and preprocessors for multimodal datasets including video, text, and sensor data.
• Build and optimize simulation infrastructure using GPU-accelerated simulators to train driving policies at scale.
• Develop sim-to-real transfer pipelines to deploy models from simulations to real-world vehicles.
• Integrate cutting-edge model architectures into scalable training pipelines in collaboration with researchers.
• Propose and implement solutions combining large language models with policy learning and reinforcement learning.
• Create robust monitoring and debugging tools to ensure reliability of workflows on large GPU clusters.

What we're looking for

• Bachelor's degree in Computer Science, Robotics, Engineering, or a related field (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 PyTorch, JAX, or TensorFlow.
• Deep familiarity with reinforcement learning algorithms including PPO, SAC, or Q-learning.
• Experience with policy learning techniques such as reward shaping, domain randomization, and curriculum learning.
• Deep understanding of GPU acceleration, CUDA programming, and cluster management tools like Kubernetes.
• Strong experience with large-scale GPU clusters, HPC environments, and job scheduling/orchestration tools like SLURM.
• Proficiency in Python and a high-performance language such as C++.