MLOps Engineer, LLM Systems (Serving, GPU Kernels, Profiling)
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 14h ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 23h ago
The date the source published, not the day we noticed it (2026-09-17). Last seen at its source just now.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
United States, Canada, United Kingdom
The description states no restriction of its own. This is the source's own tag.
Pay
$90–120/hr
Read out of the job description by us, not from a structured field. Shown in the posting's own currency and period; we never convert.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
workable employer's own board first seen 14h ago · last seen just now
The listing
This role is for one of our clients
Compensation: $90-$120 per hour
Join a leading AI lab's cutting-edge GenAI team and help build foundational AI models from the ground up. We're seeking MLOps Engineers with hands-on experience in large language model infrastructure across any of four areas: GPU kernel programming, performance profiling and trace analysis, debugging accelerated and distributed workloads, and high-throughput inference serving. This role involves AI model training and evaluation work, including writing and assessing MLOps and ML systems tasks and solutions to generate high-quality training data for frontier AI systems.
Requirements
Key Responsibilities
- Design challenging, domain-relevant tasks across four areas, GPU kernels, performance profiling, debugging, and inference serving, and write accurate, well-structured solutions to them.
- Guide research and engineering teams to close knowledge gaps and improve AI model performance on ML systems, training infrastructure, and framework-level topics.
- Evaluate MLOps and ML systems tasks and solutions, and provide clear, written technical feedback that stands up to reviewer scrutiny.
- Develop guidelines and detailed rubrics or evaluation frameworks covering kernel-level optimization, profiler output interpretation, distributed systems reasoning, and serving throughput and latency trade-offs.
- Collaborate with other subject matter experts to keep training data consistent and accurate.
Core Qualifications
- 2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or GPU and accelerator performance engineering. This is a hands-on systems role rather than an applied modelling or data science one.
- Practical experience in at least one of the following, with more than one a strong plus: writing or optimizing custom GPU kernels (CUDA, Triton, Pallas); performance profiling and trace analysis (Kineto, torch.profiler, Nsight, XLA or JAX profiler); debugging distributed or accelerator-bound workloads; serving large language models at scale (vLLM, SGLang, TensorRT-LLM, Ray Serve, KV cache, paged attention, continuous batching).
- Working production experience with JAX and/or PyTorch. Framework-level depth is a strong plus: custom operators, distributed training (FSDP, DDP, DeepSpeed, Megatron), or compiler and graph-level work.
- Familiarity with modern accelerators such as A100, H100, B200 or TPU, and the ability to reason about throughput, latency and memory trade-offs.
- Demonstrable career progression.
- Ability to engage reliably for at least 40 hours/week during weekdays.
- Strong written communication skills and the ability to explain complex technical decisions clearly.