Senior AI Infrastructure Engineer - Model Training

Highlights

• Turn thousands of hours of multimodal driving data into training throughput.
• Power driverless long-haul trucking between Dallas and Houston by end of 2026.
• Convert petabytes of raw driving logs into training-ready, streamable formats.

What they offer

• Equity and annual bonuses
• Medical, Dental, and Vision plans including infertility benefits
• Flexible PTO, 10 paid holidays, and generous parental leave
• Dog-friendly office with free catered lunch, stocked kitchen, and free EV charging
• Disability, Life Insurance, Legal Services and supplemental insurance

Ideal candidate

An ML systems engineer obsessed with GPU efficiency who measures impact in throughput and loves building fast, reliable infrastructure that lets physical-world AI train faster and improve continuously.

Requirements

• BS, MS, or PhD in Computer Science or related field with at least 2-3 years industry experience in ML systems or infrastructure
• Hands-on experience with distributed training frameworks and parallelism trade-offs (PyTorch DDP/FSDP, DeepSpeed, Megatron, NCCL)
• Experience building high-performance data pipelines for large-scale training including streaming formats, sharding, and storage-aware loading
• Deep understanding of GPU performance including mixed precision, memory hierarchy, kernel fusion, profiling, and interconnects
• Strong Python skills and proficiency in PyTorch internals; C++/CUDA/Triton a plus