ML Systems Engineer

What you'll do

• Build and optimize training and reinforcement learning infrastructure, inference and serving systems, and distributed runtimes.
• Develop secure sandboxing and execution environments; optimize memory, GPU kernels, and communication.
• Improve performance, scalability, reliability, and efficiency across the ML systems stack.

What they ask for

• Strong systems programming and performance engineering skills, with experience building high-performance ML infrastructure at scale.
• Deep expertise in at least one area: training, distributed runtime, inference, sandboxing, GPU kernels, or GPU communication.
• Strong coding ability and engineering judgment; ability to own complex technical problems end-to-end.
• Bachelor’s degree or similar experience.

Nice to have

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

Pay

$250,000-$350,000 USD base salary

Tools

Megatron-LM, Ray, SGLang, CUDA, Triton, CUTLASS, NCCL, InfiniBand

AI focus

Build agentic infrastructure for large-scale training, inference, and reinforcement learning; work effectively with AI agents to design, implement, test, and debug complex systems.