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ML Systems Engineer

Periodic Labs· Engineering
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Menlo Park, CA$250k - $350kFull-timePosted 5mo ago

About the role

ML Systems Engineer to build and optimize large-scale training, inference, and reinforcement learning infrastructure; own critical pieces of the ML systems stack to maximize performance, scalability, reliability, and productivity for engineers and AI agents.

Responsibilities

  • Build and optimize large-scale training and reinforcement learning infrastructure while ensuring its correctness
  • 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
  • Improve scalability, reliability, and efficiency across the ML systems stack

Qualifications

  • Strong systems programming and performance engineering skills
  • 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
  • High ownership, fast execution, and a passion for pushing the frontier of AI systems and accelerating scientific discovery
  • Deep expertise in at least one of: Training (Megatron-LM, distributed training systems), Distributed Runtime (Ray), Inference (SGLang), Sandboxing, GPU Kernels (CUDA, Triton, etc.), or GPU Communication (NCCL, InfiniBand, etc.)

Skills

Required:Systems programmingLarge-scale ML infrastructureEnd-to-end ownership
Last updated Sep 30, 2026. Interested? Apply on the company site.

About Periodic Labs

From bits to atoms.

Periodic Labs is an artificial intelligence research company pioneering the next leap in scientific discovery. We operate at the intersection of machine learning and scientific experimentation under the thesis that that true understanding of the universe is only possible by interacting and experimenting on it.

artificial intelligencescientific discoverymachine learningscientific experimentation
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