Research Engineer - AI/RL Infrastructure

Responsibilities

• Design and build training and evaluation infrastructure to support AI research directions, orchestrating massive GPU clusters to process PBs of multimodal sensor data
• Build robust benchmarking, continuous evaluation, and regression tracking systems to measure model performance across diverse, long-tail real-world driving distributions
• Develop large-scale data sampling, dataset generation, and advanced data curation pipelines, leveraging state-of-the-art AI models to power a closed-loop data flywheel
• Enable high-throughput distributed training across heterogeneous cloud environments, focusing on reliability, efficiency, and cost-aware scaling
• Collaborate closely with AI research, autonomy, and platform teams to translate cutting-edge research into production-ready systems

Requirements

• Experience building and operating production-grade software systems across the full machine learning lifecycle, including training, evaluation, data, and deployment
• Opinions about building a company-wide platform for ML training, evaluation, and deployment
• Experience with performance engineering and compute acceleration for large-scale ML training, including profiling, bottleneck analysis, and optimization
• Strong systems-level debugging skills to diagnose and resolve issues in large-scale distributed training, spanning model code, data pipelines, runtimes, and cluster infrastructure
• Deep familiarity with the open-source ML and systems ecosystem, with judgment on when to adopt open source versus build in-house
• Technical experience in: PyTorch, CUDA, Ray, Flyte, Kubernetes

Nice to Have

• Industry experience on relevant topics (self-driving application preferred)

Compensation

Base salary range: $126,000 - $423,000 USD annually, plus equity and benefits.