About this opportunity
Confidential lists this MLOps Engineer, LLM Performance (Kernels, Profiling, Serving) opportunity in Columbine, Colorado. Review the employer’s description below for duties, qualifications and application requirements.
Job description
Role Overview
Help advance foundational large language models by creating and evaluating high-quality ML systems training data. This hands-on role focuses on accelerator kernels, performance profiling, distributed-workload debugging, and high-throughput inference serving, with particular value placed on JAX, XLA, and TPU expertise.
Key Responsibilities
Design challenging and adversarial ML systems tasks covering kernels, profiling, debugging, and inference serving, including problems that reveal frontier-model failures in accelerator code such as TPU Pallas kernels.
Write accurate, well-structured solutions for ML systems tasks used in AI model training and evaluation.
Help research and engineering teams address knowledge gaps and improve model performance across ML systems, compilers, and training infrastructure.
Evaluate technical tasks and solutions, providing clear written feedback suitable for expert review.
Create detailed guidelines and rubrics for kernel optimization, profiler-output interpretation, distributed-systems reasoning, and serving throughput and latency trade-offs.
Collaborate with subject matter experts to maintain consistent, accurate training data.
Qualifications
At least 2 years of hands-on professional experience in ML systems, ML infrastructure, model serving, or accelerator performance engineering.
Hands-on systems engineering background rather than an applied modeling or data science focus.
Practical experience in at least one of the following areas, with experience across multiple areas preferred: custom kernel development or optimization using Pallas, Triton, CUDA, or CUTLASS; profiling and trace analysis using JAX/XLA Profiler, Kineto, torch.profiler, or Nsight; debugging distributed or accelerator-bound workloads; or large-scale LLM serving with vLLM, SGLang, TensorRT-LLM, JetStream, KV cache, paged attention, or continuous batching.
Production experience with JAX and/or PyTorch. Depth in custom operators, sharding, distributed training using pjit, shard_map, FSDP, Megatron, or MaxText, or compiler and graph-level work with XLA, StableHLO, torch.compile, or MLIR is strongly valued.
Experience with accelerators such as TPU v4 through v6e, H100, B200, Trainium, or MI300, 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.
Work Terms
Fully remote and open to candidates worldwide.
Full-time, 40 hours per week during weekdays. Candidates must be able to commit without conflicting engagements.
Engagement may be administered directly or through a local employer-of-record partner, depending on the candidate''s location.
Compensation
Hourly compensation of $90 to $120.
Eligibility
Applicants are considered without discrimination based on race, religion, color, national origin, sex, pregnancy, childbirth, reproductive health decisions, related medical conditions, sexual orientation, gender identity or expression, age, protected-veteran status, disability, genetic information, political views or activity, or any other legally protected characteristic.
Who can apply
Review the original listing for work authorization, qualifications and employer requirements.