Bachelor's or Master's degree in Computer Science, Computer/Electrical Engineering, or related field.
3+ years in AI performance engineering with experience leading large-scale projects.
Deep understanding of GPU architecture and performance characteristics.
Proficiency with profiling tools like Nsight Systems and PyTorch Profiler.
Knowledge of collective communication and modern networking concepts.
Strong programming skills in Python and CUDA/C++.
Experience with debugging performance regressions at scale.
Responsibilities
Optimize training performance for 100B+ parameter models across extensive GPU clusters.
Collaborate on decisions regarding accelerators, cluster operations, and hardware procurement.
Write and optimize custom GPU kernels using Triton/CUDA.
Develop tools and dashboards for performance monitoring and regression analysis.
Enhance data loading and preprocessing pipelines to maximize I/O efficiency.
Improve checkpointing and fault tolerance for large-scale jobs.
Work with researchers to co-design scalable model architectures and training techniques.
Benefits
Flexible work hours and remote work options are typically available.
Access to cutting-edge technology and resources in AI and ML.
Opportunities for professional development and continued education.
Collaborative and innovative work environment focused on autonomy.
Potential for contributions to open-source ML projects.
Full Job Description
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. This role is focused on improving distributed training frameworks for large scale model training, optimizing GPU kernels, exploring the relative gains of different accelerator types and co-designing our models to maximize utilization of our hardware.
Responsibilities
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
Improve checkpointing, fault tolerance, and elastic restart so large jobs recover quickly from node failures without losing significant wall-clock time
Partner with researchers to co-design model architectures and training recipes that are performant at scale (e.g., activation checkpointing strategies, mixed precision, sequence packing)
Extend and contribute to kernel compilers (e.g., Triton, Gluon) to improve iteration speed and enable targeting of custom/non-NVIDIA accelerators
Build and extend agentic systems that automatically generate, benchmark, and iterate on custom kernels
Evaluate emerging accelerator architectures (AMD, TPU, SRAM-based ASICs, and other novel hardware) for fit with our training workloads, and lead proof-of-concept ports/benchmarks
Explore different model/data parallelisms (FSDP, context parallel, expert parallel, etc.) to determine optimal configuration per model size.
Requirements
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).
Strong Python and CUDA/C++ skills; comfortable reading and modifying framework internals
Experience debugging performance regressions and instability at scale (stragglers, hangs, OOMs, numerical divergence)
Experience defining and reasoning about hardware-efficiency metrics (MFU/HFU) and using them to drive optimization priorities
Bonus Qualifications
Experience with heterogeneous or multi-datacenter training setups and cross-cluster orchestration
Contributions to open-source ML systems projects (PyTorch, Megatron-LM, vLLM, DeepSpeed, JAX, etc.)
Exposure to non-NVIDIA accelerators (AMD GPUs, TPU/Trainium/Inferentia, or custom silicon) and heterogeneous fleet management.
The US base salary range for this full-time position is between $200,000 - $400,000 annually.
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.
