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# MLOps Engineer, LLM Systems

- **Company:** LAKE ST LLC
- **Location:** Lake Saint Croix Beach, MN, United States
- **Experience:** Experienced
- **Salary:** $187,200.0 - $249,600.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Profiling, Nvidia CUDA, Software Debugging, Linux Kernel, Pytorch, Large Language Models, Low Latency, Machine Learning Operations, TensorRT
- **Published:** September 27, 2026
- **Apply:** https://www.juju.com/job/18_5339_277779

## About the Role

* 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.

## Description

* 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.


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