Onsite | JAX ML Infrastructure Evaluation Consultant — $55–$85/hour

We are sharing a specialised full-time contingent opportunity for professionals experienced in JAX, ML infrastructure, MLOps, distributed training systems, model training workflows, kernel-level optimization, and structured technical evaluation. This role supports current and upcoming technical opportunities focused on AI model training and evaluation, ML systems task design, infrastructure reasoning assessment, and high-quality technical feedback. Selected professionals will design challenging ML infrastructure tasks, write accurate solutions, evaluate technical outputs, and develop rubrics for assessing training pipeline design, distributed systems reasoning, and framework-level optimization.

Key Responsibilities Professionals in this role may contribute to: ML Infrastructure Task Design Design challenging, domain-relevant tasks involving MLOps, ML infrastructure, model training systems, distributed training workflows, and ML framework-level concepts Write accurate, well-structured technical solutions for ML systems and infrastructure problems Create tasks that test practical reasoning around training pipelines, scalability, reliability, and production ML workflows Support high-quality technical data development through precise, realistic, and expert-level task design JAX & Kernel-Level Technical Evaluation Evaluate technical tasks and solutions involving JAX, ML systems, custom kernel workflows, Pallas, Triton, and related optimization topics Assess whether solutions demonstrate correct reasoning, technical accuracy, implementation awareness, and clear engineering judgment Identify incomplete reasoning, incorrect assumptions, weak system design, missing constraints, or optimization gaps Provide clear written feedback on solution quality, technical correctness, and improvement areas Rubric Development & Technical Quality Control Develop detailed rubrics and evaluation frameworks for ML infrastructure, training pipeline design, distributed systems reasoning, and kernel-level optimization tasks Apply structured review criteria consistently across technical assignments Collaborate with other subject matter experts to support consistency, accuracy, and quality across project materials Explain complex technical decisions clearly through concise, well-organized written feedback Ideal Profile Strong candidates may have: 2+ years of dedicated professional experience in ML infrastructure, MLOps, ML systems engineering, or related technical engineering work Hands-on production experience using JAX at scale Experience writing, optimizing, or evaluating custom GPU kernels using Pallas, Triton, or comparable kernel-level tools Strong understanding of AI model training workflows, training infrastructure, distributed systems, and ML framework-level performance considerations Demonstrable career progression in technical engineering, ML systems, infrastructure, or applied AI work Strong written communication skills with the ability to explain complex technical decisions clearly Reliable weekday availability for a full-time 40-hour engagement Educational Background Academic or professional background in computer science, machine learning, electrical engineering, applied mathematics, data science, software engineering, or a related technical field is highly relevant Professional experience in ML infrastructure, MLOps, model training systems, distributed computing, GPU optimization, performance engineering, or AI systems development is especially valuable Experience with production ML environments, large-scale training workflows, model evaluation pipelines, or technical research engineering may support project fit Advanced technical experience may be considered alongside formal education depending on project requirements Nice to Have Experience developing or evaluating ML systems tasks, technical interviews, engineering assessments, training materials, or structured evaluation rubrics Familiarity with accelerator programming, GPU performance optimization, XLA, distributed training, model parallelism, or large-scale training infrastructure Experience working with research engineering teams, model training platforms, infrastructure tooling, or production AI systems Comfort identifying subtle technical gaps in ML systems reasoning, framework-level implementation, or kernel optimization logic Strong ability to maintain precision and consistency across demanding technical review tasks Why This Opportunity Apply JAX, MLOps, and ML infrastructure expertise to high-impact technical evaluation work Contribute to advanced AI model training and evaluation workflows through expert task design and solution review Use hands-on systems engineering judgment in a structured technical assessment environment Work on full-time assignments aligned with ML systems, kernel optimization, and infrastructure reasoning strengths Competitive hourly compensation for specialised technical expertise Contract Details Full-time contingent technical engagement United States-based professionals only, depending on project requirements Expected commitment of 40 hours per week during weekdays Candidates should be able to engage reliably without conflicting professional commitments during the active project period Competitive rates of $55–$85 per hour depending on expertise, technical depth, availability, and project scope Final engagement structure, payment setup, and role terms will be confirmed during the matching or offer process Projects may be extended, shortened, or adjusted depending on scope and performance Work will not involve access to confidential or proprietary information from any employer, client, or institution About the Platform This opportunity is available through 24-MAG LLC. 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