Remote | MLOps & ML Systems Engineer — $60–$100/hour

We are sharing a specialised part-time consulting opportunity for MLOps and ML systems professionals experienced in training infrastructure, modern ML frameworks, JAX, PyTorch, kernel-level programming, and structured technical evaluation.

This role supports current and upcoming remote consulting opportunities focused on ML infrastructure review, MLOps task development, framework-level engineering, model training workflow evaluation, kernel-level optimization, and high-quality project execution.

Selected professionals will apply hands-on ML systems expertise to design challenging technical tasks, evaluate solutions, review training pipeline reasoning, and provide structured feedback across advanced machine learning engineering workflows.

Key Responsibilities Professionals in this role may contribute to: MLOps & Training Infrastructure Review Design and evaluate domain-relevant tasks involving MLOps, ML systems, and training infrastructureReview solutions related to training pipeline design, distributed systems reasoning, model training workflows, and infrastructure-level decision-makingIdentify gaps in technical reasoning, unclear assumptions, incomplete solutions, or weak engineering tradeoff analysisSupport review of technical materials involving modern ML frameworks, system design, and model development workflows Framework-Level & Kernel Programming Evaluation Write, assess, and reason about technical tasks involving JAX, PyTorch, Pallas, Triton, and related ML engineering toolsEvaluate code, explanations, and solutions involving kernel-level programming and performance-oriented ML systems workReview technical decisions involving framework behavior, training efficiency, GPU utilization, and implementation qualityApply strong engineering judgment to assess correctness, clarity, scalability, and practical feasibility Rubric Development & Technical Feedback Develop detailed rubrics and evaluation frameworks for MLOps, ML systems, distributed training, and kernel-level tasksProvide clear written technical feedback explaining solution quality, reasoning gaps, and improvement areasCollaborate with other subject matter experts to support consistency and accuracy across technical review workMaintain a high standard of precision, documentation quality, and structured evaluation across submitted materials Ideal Profile Strong candidates may have: 2+ years of dedicated professional experience in ML infrastructure, MLOps, ML systems engineering, or a closely related technical fieldHands-on production experience with JAX and/or PyTorch at scaleExperience writing or optimizing custom GPU kernels using Pallas, Triton, or comparable kernel programming toolsStrong understanding of model training infrastructure, distributed systems, framework-level behavior, and ML engineering workflowsDemonstrable career progression in technical engineering rolesStrong written communication skills and ability to explain complex technical decisions clearlyAbility to work independently in a remote, project-based environment Educational Background A degree in computer science, computer engineering, electrical engineering, applied mathematics, machine learning, data science, or a related technical field is helpfulProfessional experience in MLOps, ML infrastructure, ML systems, distributed training, GPU programming, or framework-level engineering is highly relevantEquivalent hands-on experience with large-scale ML systems, production training workflows, or kernel-level optimization may also be valuable Nice to Have Experience with distributed training systems, model training pipelines, GPU optimization, performance analysis, or large-scale ML infrastructureFamiliarity with Pallas, Triton, CUDA-adjacent workflows, XLA, JAX internals, PyTorch internals, or compiler-adjacent ML systems workExperience writing technical rubrics, evaluation frameworks, benchmark tasks, or expert-level engineering assessmentsComfort reviewing complex technical solutions and explaining tradeoffs with clarity and precisionAvailability for high-commitment project work, potentially up to 40 hours per week depending on project scope Why This Opportunity Apply M precisionAvailability for high-commitment project work, potentially up to 40 hours per week depending on project scope Why This Opportunity Apply MLOps and ML systems expertise to structured remote project workContribute to high-quality technical task design, solution evaluation, and training infrastructure reviewWork on flexible assignments aligned with your JAX, PyTorch, kernel programming, and ML systems backgroundUse your engineering judgment to evaluate complex technical reasoning and framework-level implementation qualityRemote structure with competitive hourly compensation Contract Details Independent contractor roleFully remote with flexible schedulingEligible professionals should be based in the United States depending on project needsHigh-commitment project availability may be required, potentially up to 40 hours per week during weekdays depending on project scopeCompetitive rates between $60–$100 per hour depending on expertise and project scopeWeekly payments via Stripe or WiseProjects may be extended, shortened, or adjusted depending on scope and performanceWork 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.

We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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