MLOps Engineer, LLM Systems

• Design challenging, domain-relevant MLOps and ML systems tasks in GPU kernels, profiling, debugging, and inference serving, then produce accurate, well-structured solutions.
• Evaluate technical tasks and solutions, providing clear written feedback that can withstand detailed review.
• Support research and engineering teams in closing knowledge gaps and improving model performance across ML systems, training infrastructure, and framework-level subjects.
• Create detailed guidelines and evaluation rubrics for kernel optimization, profiler-output interpretation, distributed-systems reasoning, and serving throughput and latency trade-offs.
• Partner with subject matter experts to maintain consistent, accurate training data.

• At least 2 years of hands-on professional experience in ML systems, ML infrastructure, model serving, or GPU and accelerator performance engineering.
• Experience in at least one of the following areas, with experience across multiple areas strongly preferred: custom GPU kernel development or optimization using CUDA, Triton, or Pallas; profiling and trace analysis using Kineto, torch.profiler, Nsight, XLA, or JAX profiler; debugging distributed or accelerator-bound workloads; or serving LLMs at scale using vLLM, SGLang, TensorRT-LLM, Ray Serve, KV cache, paged attention, or continuous batching.
• Production experience with JAX and/or PyTorch. Framework-level expertise in custom operators, distributed training with FSDP, DDP, DeepSpeed, or Megatron, or compiler and graph-level work is preferred.
• Familiarity with A100, H100, B200, or TPU accelerators, including the ability to assess throughput, latency, and memory trade-offs.
• Demonstrable career progression, strong written communication, and the ability to explain complex technical decisions clearly.
• This is a systems-focused position, not an applied modeling or data science role.

Work Terms

• Hourly W-2 employment with placement on an extended workforce team supporting a leading AI lab.
• Full-time, 40-hour-per-week weekday commitment. Candidates must be able to work without conflicting engagements or other concurrent work commitments.
• Available to candidates located in Canada, the United Kingdom, or the United States.

Compensation

$90 to $120 per hour.