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Job description
Role Overview
Help develop foundational large language models by creating and evaluating rigorous ML systems work that improves frontier AI training data. This hands-on infrastructure role focuses on GPU kernels, performance profiling and trace analysis, accelerated and distributed workload debugging, and high-throughput inference serving.
Key Responsibilities
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.
Qualifications
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.
Equal Employment Opportunity
Employment decisions are made without discrimination based on race, religion, color, national origin, sex, including pregnancy, childbirth, reproductive-health decisions, or 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.