Remote | mlops engineer, llm systems (serving, gpu kernels, profiling) — $90–$120/hour

We are sharing a full-time opportunity for experienced MLOps Engineers with hands-on expertise in large language model infrastructure, GPU acceleration, performance profiling, distributed-system debugging, and high-throughput inference serving to contribute to advanced AI training and evaluation initiatives. Selected professionals will develop challenging ML-systems tasks, produce technically rigorous reference solutions, evaluate model-generated outputs, and help establish evaluation standards across GPU kernels, profiling, debugging, and LLM serving. This is a hands-on systems role intended for engineers with production infrastructure experience rather than primarily applied modelling or data-science backgrounds.

Key Responsibilities

• GPU Kernels & Accelerator Engineering
  • Design technically challenging tasks involving GPU and accelerator workloads
  • Develop solutions covering CUDA, Triton, Pallas, or comparable kernel technologies
  • Evaluate kernel-level optimisation approaches for correctness and efficiency
  • Analyse memory, compute, and hardware-utilisation trade-offs
  • Apply practical accelerator engineering judgement to model-generated solutions
• Performance Profiling & Trace Analysis
  • Develop tasks involving performance profiling and trace interpretation
  • Analyse outputs from tools such as Kineto, torch.profiler, Nsight, XLA, or JAX profilers
  • Identify bottlenecks across compute, memory, communication, and scheduling
  • Evaluate throughput, latency, and utilisation characteristics
  • Produce clear reference analyses explaining observed performance behaviour
• Distributed Systems & Workload Debugging
  • Design scenarios involving distributed or accelerator-bound ML workloads
  • Diagnose failures across training and inference infrastructure
  • Evaluate reasoning around FSDP, DDP, DeepSpeed, Megatron, and related systems
  • Review framework-level and distributed-system troubleshooting approaches
  • Identify technically plausible but incorrect explanations or proposed fixes
• LLM Inference & Serving
  • Develop and assess tasks involving high-throughput LLM serving
  • Apply expertise with vLLM, SGLang, TensorRT-LLM, Ray Serve, or comparable platforms
  • Evaluate KV-cache, paged-attention, and continuous-batching strategies
  • Analyse serving architectures for latency, throughput, memory, and scalability trade-offs
  • Review production-oriented approaches to large-scale inference deployment
• Technical Evaluation & Research Collaboration
  • Evaluate MLOps and ML-systems tasks and proposed solutions
  • Provide precise written feedback that can withstand technical review
  • Develop detailed rubrics and evaluation frameworks for systems-level work
  • Help research and engineering teams close technical knowledge gaps
  • Collaborate with subject-matter experts to maintain consistent training-data quality

Ideal Profile

• 2+ years of hands-on professional experience in ML systems, ML infrastructure, model serving, or accelerator-performance engineering
• Strong practical experience in at least one of GPU kernel programming, performance profiling, distributed debugging, or high-throughput inference serving
• Production experience with JAX and/or PyTorch
• Familiarity with CUDA, Triton, Pallas, or comparable accelerator-programming technologies
• Experience with profiling tools such as Kineto, torch.profiler, Nsight, XLA, or JAX profiler
• Experience debugging distributed or accelerator-bound workloads
• Familiarity with vLLM, SGLang, TensorRT-LLM, Ray Serve, KV cache, paged attention, or continuous batching
• Framework-level experience with custom operators, FSDP, DDP, DeepSpeed, Megatron, compiler, or graph-level work is highly valuable
• Familiarity with accelerators such as A100, H100, B200, or TPU
• Ability to reason precisely about throughput, latency, memory, and compute trade-offs
• Demonstrable professional progression in ML infrastructure or systems engineering
• Strong written communication and ability to explain complex technical decisions clearly

Engagement Details

• Full-time 40-hour-per-week engagement
• Remote — Canada, United Kingdom, and United States
• Compensation: $90–$120/hour
• Reliable weekday availability is required
• The engagement requires no conflicting or concurrent professional engagements
• Work will involve ML-systems task development, reference-solution authoring, technical evaluation, rubric development, and research collaboration
• Primary technical areas include GPU kernels, performance profiling, distributed debugging, and high-throughput LLM inference
• Assignments may involve PyTorch, JAX, CUDA, Triton, distributed-training frameworks, modern accelerators, and production serving systems
• Projects may be extended, shortened, or concluded depending on project needs and performance
• H1-B and STEM OPT candidates cannot currently be supported
• Employment classification should be confirmed during onboarding because the source materials contain conflicting W-2 and independent-contractor language
• Work must be completed without using confidential or proprietary information belonging to any employer, client, institution, or other third party

About the Platform

This opportunity is available through 24-MAG LLC. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams. By submitting this application, you acknowledge that your information may be processed by 24-MAG LLC for recruitment and opportunity matching in accordance with our Privacy Policy: https://www.24-mag.com/privacy-policy