ML Systems Engineer

The role

You will build agentic infrastructure for large-scale training, inference, and reinforcement learning. You will own pieces of the ML systems stack to maximize performance, scalability, reliability, and productivity.

What you will do

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

Required 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
• 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
• Deep expertise in at least one of the following: Training (Megatron-LM), Distributed Runtime (Ray), Inference (SGLang), Sandboxing (secure execution environments, containers, virtualization, or code s
• Bachelor’s degree or similar experience

Nice to have

• Familiarity with TorchTitan, FSDP, veRL, Slime, or other distributed training systems
• Familiarity with Monarch or other distributed execution frameworks
• Familiarity with vLLM, TensorRT-LLM, or production LLM serving systems

Before you apply

• We sponsor visas

About the company

Periodic Labs is an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond.