Onsite | PyTorch ML Systems Evaluation Consultant - $55-$85/hour We are sharing a specialised full-time contingent opportunity for professionals experienced in PyTorch, 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, performance, and production ML workflows • Support high-quality technical data development through precise, realistic, and expert-level task design PyTorch & Kernel-Level Technical Evaluation • Evaluate technical tasks and solutions involving PyTorch, ML systems, custom kernel workflows, Triton, Pallas, 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 PyTorch at scale • Experience writing, optimizing, or evaluating custom GPU kernels using Triton, Pallas, 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, distributed training, model parallelism, large-scale training infrastructure, or framework-level optimization • 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 PyTorch, 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 • Onsite 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