MLOps Engineer, LLM Systems, Serving, GPU Kernels, Profiling

Weekday (YC W21)

. Design challenging, domain-relevant tasks across GPU kernels, performance profiling, debugging, and inference serving, and write accurate, well-structured solutions .

Posted 9/18/2026part-timeRemote • United States, Canada, United KingdomJuniorMid-Level💰 $90 - $120 per hourWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in ML Systems and GPU Performance Engineering, with a strong focus on optimizing custom GPU kernels and evaluating MLOps tasks. Proven ability to communicate complex technical concepts clearly and collaborate effectively with research and engineering teams.

Highest-signal resume keywords

ML Systems ExperienceGPU Kernel Optimization (CUDA, Triton, Pallas)Performance Profiling (Kineto, torch.profiler, Nsight)Model Serving (vLLM, TensorRT-LLM, Ray Serve)JAX and/or PyTorch Proficiency

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

ML SystemsGPU Performance EngineeringCustom GPU KernelsPerformance ProfilingDebugging Distributed WorkloadsModel ServingThroughput and Latency ReasoningMLOpsAI Model TrainingEvaluation Frameworks

Soft Skills

Strong Written CommunicationCollaboration

Tools & Technologies

CUDATritonPallasKinetoTorch.profilerNsightXLAJAXVLLMTensorRT-LLM

Industry Keywords

AI Model PerformanceTraining InfrastructureDistributed SystemsPerformance ProfilingAccelerator Performance

Tech Stack

Tools & technologies

Distributed SystemsPyTorchRay

About the role

Key responsibilities & impact

Design challenging, domain-relevant tasks across GPU kernels, performance profiling, debugging, and inference serving, and write accurate, well-structured solutions
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
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 subject matter experts to keep training data consistent and accurate
Contribute to AI model training and evaluation work by writing and assessing MLOps and ML systems tasks and solutions for frontier AI training data

Requirements

What you’ll need

2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or GPU and accelerator performance engineering
Practical experience in at least one of: 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
Familiarity with modern accelerators such as A100, H100, B200 or TPU
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 ability to explain complex technical decisions clearly