ML Systems Engineer
AI research and deployment company building AI scientists and autonomous laboratories for physical-science discovery.
About Periodic Labs
Periodic Labs develops specialized AI models and high-throughput autonomous labs that run and analyze physical experiments, initially for materials discovery including superconductors, magnets, and semiconductor applications.
Skills
Candidate Availability
Required and preferred rules are kept separate and reflect the wording in the original posting.
About the Role
You will build and optimize large-scale training and reinforcement learning infrastructure, develop high-performance inference systems, and design distributed runtimes. You will improve performance, scalability, reliability, sandboxing, GPU kernels, memory use, and communication efficiency across the ML systems stack.
Requirements
- Systems programming
- Performance engineering
- High-performance ML infrastructure
- Complex technical problem ownership
- Coding
- Engineering judgment
- Megatron-LM or Ray or SGLang or secure execution environments or CUDA or Triton or CUTLASS or CuTe or NCCL or NVLink or InfiniBand or RDMA
Responsibilities
- Build and optimize large-scale training and reinforcement learning infrastructure
- Develop high-performance inference and serving systems
- Design distributed runtimes and scheduling systems
- Build secure large-scale sandboxing and execution environments
- Optimize memory GPU kernels and communication
- Improve scalability reliability and efficiency across the ML systems stack
Benefits
- Visa sponsorship