ML Systems Engineer

About Periodic Labs

We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible.

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.

We’re looking for exceptional ML Systems Engineers to build the agentic infrastructure powering our large-scale training, inference, and reinforcement learning. You’ll own critical pieces of the ML systems stack to maximize performance, scalability, reliability, and productivity for both engineers and AI agents.

What You'll Do

• 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

What We're Looking For

• 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

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You should have deep expertise in at least one of the following:

• Training: Strong experience building, debugging and optimizing large-scale training systems with Megatron-LM. Familiarity with TorchTitan, FSDP, veRL, Slime, or other distributed training systems is a plus.

• Distributed Runtime: Strong experience with Ray. Familiarity with Monarch or other distributed execution frameworks is a plus.

• Inference: Strong experience with . Familiarity with vLLM, TensorRT-LLM, or production LLM serving systems is a plus.