Remote | ML Infrastructure & Kernel Optimization Engineer — $65–$105/hour

We are sharing a specialised full-time consulting opportunity for US-based MLOps and ML systems engineers with production experience in JAX, PyTorch, distributed training infrastructure, and custom GPU kernel development using Pallas or Triton. This role supports a high-impact generative AI initiative focused on developing and evaluating advanced ML infrastructure tasks for frontier model training. Selected engineers will design technically challenging problems, produce rigorous solutions, assess model-generated outputs, and help establish evaluation standards across training pipelines, distributed systems, framework-level optimisation, and GPU kernel performance. Key Responsibilities ML Infrastructure & Training Systems Analyse and improve machine learning training infrastructure, deployment workflows, and model-development systems Guide research and engineering teams on MLOps, distributed training, and ML framework-level challenges Evaluate training-pipeline architecture, scalability, reliability, and performance Identify technical gaps affecting model training, experimentation, and infrastructure efficiency Technical Task & Solution Development Design challenging tasks covering MLOps, ML systems, training infrastructure, and framework-level engineering Write accurate, technically rigorous, and well-structured solutions Develop realistic scenarios involving distributed systems, accelerator utilisation, and production ML workflows Ensure tasks reflect practical engineering challenges encountered in advanced AI environments JAX, PyTorch & GPU Kernel Evaluation Evaluate technical work involving JAX and PyTorch at production scale Review custom GPU kernels written or optimised using Pallas or Triton Assess kernel correctness, memory access patterns, computational efficiency, and hardware utilisation Analyse framework-level implementation choices and identify opportunities for performance improvement Evaluation Frameworks & Technical Feedback Compare alternative technical solutions and determine which approach is more accurate and effective Provide clear written feedback on correctness, system design, scalability, and optimisation quality Develop detailed rubrics for evaluating training pipelines, distributed systems reasoning, and kernel-level implementations Collaborate with other technical specialists to maintain consistency across evaluation standards and training data Ideal Profile Strong candidates may have: At least 2 years of dedicated professional experience in MLOps, ML infrastructure, or ML systems engineering Production experience with JAX, PyTorch, or both at meaningful scale Hands-on experience writing or optimising custom GPU kernels using Pallas or Triton Strong knowledge of model-training pipelines, distributed systems, accelerators, and performance optimisation Experience working within a recognised technology, AI research, or high-performance engineering organisation Demonstrable professional growth and increasing technical responsibility Strong written communication and the ability to explain complex engineering decisions clearly Reliable availability for a full-time, 40-hour weekday schedule Educational Background A degree in computer science, machine learning, electrical engineering, applied mathematics, or a related technical field is highly relevant Graduate-level education in machine learning systems, distributed computing, or high-performance computing may be helpful Equivalent professional experience in production ML infrastructure may also be considered Advanced technical work involving GPU programming, compiler systems, or large-scale model training is especially valuable Nice to Have Experience supporting large language model or generative AI training environments Familiarity with distributed training frameworks, accelerator orchestration, and multi-host systems Knowledge of XLA, CUDA, compiler optimisation, or low-level performance engineering Experience benchmarking GPU workloads and diagnosing training-performance bottlenecks Familiarity with model-evaluation pipelines, technical annotation, or structured training-data development Previous involvement in technical review, engineering mentorship, or rubric development Experience collaborating with research scientists and infrastructure engineering teams Why This Opportunity Contribute to advanced generative AI and large-scale model-training initiatives Apply deep expertise in JAX, PyTorch, Pallas, Triton, and ML infrastructure Work on challenging problems spanning training systems, distributed computing, and GPU optimisation Influence the quality of technical training data used in frontier AI development Join a full-time remote engagement with competitive hourly compensation Contract Details Full-time W-2 contingent employment arrangement Fully remote role available to candidates based in the United States Expected commitment of 40 hours per week during weekdays This engagement requires full professional availability without conflicting employment or external commitments Competitive rates between $65–$105 per hour depending on expertise and project scope Immediate availability is preferred Work may include onboarding, technical calibration, and ongoing quality-review activities Project scope and duration may be adjusted according to programme requirements and performance About the Platform This opportunity is available through 24-MAG LLC. 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