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Foster City, California, United States
Onsite
Design, implement, and operate cutting-edge ML training performance optimization techniques — distributed training (data/tensor/pipeline parallelism), mixed-precision, gradient checkpointing, and GPU kernels — to scale our VLM, VLA, and Foundational models training efficiency.
Work on RL framework and RL performance optimization for driving foundation model
Profile and debug training throughput bottlenecks across the compute, compiler, framework, and cluster-networking stack.
Collaborate closely with ML researchers, data engineers, and hardware engineers to define requirements and align on architectural decisions for large-scale training infrastructure.
Implement custom kernels and build benchmarks to track and validate training-performance optimizations across the stack.
Note: You do not have to meet all the requirements below to be considered for this position:
4+ years of total experience, including 2+ years working on large-scale model training platforms.
Experience with distributed training frameworks (Megatron-LM, TorchTitan), efficiently utilizing GPU/accelerator clusters at scale.
Experience with RL framework such as Verl/MILES and RL algorithms(GRPO, PPO)
Experience with collective communication libraries (e.g., NCCL) and optimizing communication/compute overlap in multi-node training.
Experience using profiling tools (NVIDIA Nsight, PyTorch Profiler, or XLA/JAX profiling) to identify and resolve training bottlenecks.
Proficient in Python or C++.
Experience with ML compilers, performance tooling, or hardware/software co-design (e.g., GPU/TPU/accelerator architectures).
