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

Job summary

Design and evaluate complex MLOps tasks related to GPU kernels, performance profiling, and inference serving. Guide research teams to improve AI model performance and develop robust evaluation frameworks for training infrastructure.

Job details

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. 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.

What you’ll do

Design and evaluate complex MLOps tasks related to GPU kernels, performance profiling, and inference serving. Guide research teams to improve AI model performance and develop robust evaluation frameworks for training infrastructure.

Requirements

Requires 2+ years of professional experience in ML systems, infrastructure, or accelerator performance engineering. Candidates must possess hands-on expertise in GPU kernel optimization, distributed systems, or large-scale model serving.

Listed skills

• Évaluation · Preferred
• Production · Preferred
• Technical · Preferred
• written communication · Preferred
• analysis · Preferred
• Organization · Preferred
• Training · Preferred
• Attention to detail · Preferred
• Teams · Preferred
• Communication · Preferred
• Communication Skills · Preferred
• Consistent · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• MLOps
• GPU Kernel Programming
• Performance Profiling
• Distributed Systems
• Inference Serving
• CUDA
• Triton
• Pallas
• PyTorch
• JAX
• vLLM
• TensorRT-LLM
• Ray Serve
• Distributed Training
• Compiler Optimization
• Large Language Modeling
• MLOps (Machine Learning Operations)
• Generative Artificial Intelligence
• Distributed Machine Learning
• Research
• Artificial Intelligence
• Communication
• Nvidia CUDA
• Debugging
• Machine Learning
• Performance Engineering
• Writing
• Data Science
• PyTorch (Machine Learning Library)
• Artificial Intelligence Infrastructure
• Machine Learning Infrastructure

Job areas

• Technology
• Software
• Engineering
• Data & Analytics
• Science & Research
• Machine Learning Operations Engineer
• Generative Artificial Intelligence Engineer
• Software Developers
• Computer and Information Research Scientists