Helix AI Engineer, Training Performance

Job summary

Responsibilities

Figure's vision is to deploy autonomous humanoids at a global scale. Our Helix team is looking for an experienced AI Training Performance Engineer to take our model training to the next level.

• Optimize training performance for a 100B+ parameter models across 100k+ GPUs.
• Collaborate with the broader team on accelerator choice, cluster topology, scheduling, and hardware procurement decisions to inform future scaling.
• Write and optimize custom kernels (Triton/CUDA).
• Build tooling and dashboards for continuous performance monitoring, regression detection, and root-cause analysis across training jobs.
• Optimize data loading and preprocessing pipelines so I/O never gates the accelerators.

What they're looking for

• Bachelor's or Master's degree in Computer Science, Computer/Electrical Engineering, or a related field.
• 3+ years in AI performance engineering, with significant time leading large-scale performance improvement projects.
• Deep understanding of GPU architecture and performance characteristics (memory bandwidth, compute-bound vs. memory-bound ops, occupancy).
• Proficiency with profiling tools (Nsight Systems/Compute, PyTorch Profiler, HTA, or similar) and ability to translate traces into concrete optimizations.
• Solid grasp of collective communication (NCCL) and modern networking concepts (RDMA, NVLink, InfiniBand/RoCE, topology-aware placement).

Tech stack

• Triton
• CUDA
• PyTorch
• Python
• C++
• vLLM
• JAX