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 infrastructure
• Review solutions related to training pipeline design, distributed systems reasoning, model training workflows, and infrastructure-level decision-making
• Identify gaps in technical reasoning, unclear assumptions, incomplete solutions, or weak engineering tradeoff analysis
• Support 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 tools
• Evaluate code, explanations, and solutions involving kernel-level programming and performance-oriented ML systems work
• Review technical decisions involving framework behavior, training efficiency, GPU utilization, and implementation quality
• Apply 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 tasks
• Provide clear written technical feedback explaining solution quality, reasoning gaps, and improvement areas
• Collaborate with other subject matter experts to support consistency and accuracy across technical review work
• Maintain 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 field
• Hands-on production experience with JAX and/or PyTorch at scale
• Experience writing or optimizing custom GPU kernels using Pallas, Triton, or comparable kernel programming tools
• Strong understanding of model training infrastructure, distributed systems, framework-level behavior, and ML engineering workflows
• Demonstrable career progression in technical engineering roles
• Strong written communication skills and ability to explain complex technical decisions clearly
• Ability 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 helpful
• Professional experience in MLOps, ML infrastructure, ML systems, distributed training, GPU programming, or framework-level engineering is highly relevant
• Equivalent 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 infrastructure
• Familiarity with Pallas, Triton, CUDA-adjacent workflows, XLA, JAX internals, PyTorch internals, or compiler-adjacent ML systems work
• Experience writing technical rubrics, evaluation frameworks, benchmark tasks, or expert-level engineering assessments
• Comfort reviewing complex technical solutions and explaining tradeoffs with clarity and precision
• Availability for high-commitment project work, potentially up to 40 hours per week depending on project scope

Why This Opportunity

• Apply M precision
• Availability 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 work
• Contribute to high-quality technical task design, solution evaluation, and training infrastructure review
• Work on flexible assignments aligned with your JAX, PyTorch, kernel programming, and ML systems background
• Use your engineering judgment to evaluate complex technical reasoning and framework-level implementation quality
• Remote structure with competitive hourly compensation

Contract Details

• Independent contractor role
• Fully remote with flexible scheduling
• Eligible professionals should be based in the United States depending on project needs
• High-commitment project availability may be required, potentially up to 40 hours per week during weekdays depending on project scope
• Competitive rates between $60–$100 per hour depending on expertise and project scope
• Weekly payments via Stripe or Wise
• 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. We connect experienced professionals with remote consulting opportunities across technical, evaluation, and project-based workstreams.

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