Periodic Labs
ML Systems Engineer
Menlo Park
H1B sponsorship available$250k-$350kDetected 84 days ago
Machine LearningLLMsAgentic AILLMOpsSystems EngineeringCommunication
About the role
- You'll work alongside some of the world's leading ML systems engineers, including leaders behind Megatron-LM, SGLang, Liger Kernel, TorchRec, CleanRL, TorchRL, and JAX-MD.
Responsibilities
- Develop high-performance inference and serving systems
- Design distributed runtimes and scheduling systems for complex ML workloads
- Build secure and large-scale sandboxing and execution environments
- Optimize memory, GPU kernels and communication for maximum throughput and end-to-end efficiency
- Experience building high-performance ML infrastructure at scale
- Ability to own complex technical problems end-to-end
- Strong coding ability and engineering judgment, including the ability to work effectively with AI agents to design, implement, test, and debug complex systems
- We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond.
Requirements
- Sandboxing: Strong experience with secure execution environments, containers, virtualization, or code sandboxing.
- GPU Kernels: Strong experience with CUDA, Triton, CUTLASS, CuTe, or custom GPU kernel development.
- GPU Communication: Strong experience with NCCL, NVLink, InfiniBand, RDMA, GPUDirect RDMA, or large-scale communication optimization.
- Strong experience with secure execution environments, containers, virtualization, or code sandboxing.
- Strong experience with CUDA, Triton, CUTLASS, CuTe, or custom GPU kernel development.
- Strong experience with NCCL, NVLink, InfiniBand, RDMA, GPUDirect RDMA, or large-scale communication optimization.
- Bachelor's degree or similar experience
Nice to have
- Familiarity with TorchTitan, FSDP, veRL, Slime, or other distributed training systems is a plus.
- Strong experience with Ray.
- Familiarity with Monarch or other distributed execution frameworks is a plus.
- Strong experience with SGLang.
- Familiarity with vLLM, TensorRT-LLM, or production LLM serving systems is a plus.
- Distributed Runtime: Strong experience with Ray.
- Inference: Strong experience with SGLang.
Compensation
- $250,000-$350,000 base + equity
Benefits
- Build and optimize large-scale training and reinforcement learning infrastructure while ensuring its correctness
Visa & Work Authorization
- Yes, we sponsor visas and will do everything we can to assist in this process with our legal support.
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This listing is sourced directly from Periodic Labs's careers page and normalized into a canonical job model.