Remote MLOps Engineer: JAX/PyTorch & Kernel Focus

Mercor; United States; On-site; USD 96,432 - 151,536; Part time

Job summary

Mercor is seeking an experienced MLOps Engineer to help scale AI research pipelines. The role centers on JAX and PyTorch, focusing on training infrastructure and kernel-level optimization. You will lead complex tasks and provide clear feedback on solutions, collaborating with research and engineering teams.

This remote contract position requires 40 hours/week and strong communication to guide projects, set rubrics, and ensure high-quality model performance across platforms.

Qualifications

• 2+ years of dedicated professional experience in ML infrastructure, MLOps, or ML systems engineering at a recognized, top-tier organization.
• Hands-on production experience with JAX and/or PyTorch at scale.
• Experience writing or optimizing custom GPU kernels using Pallas (JAX) or Triton.
• Demonstrable career progression.
• Ability to engage reliably for at least 40 hours/week during weekdays.
• Strong written communication skills and the ability to explain complex technical decisions clearly.

Responsibilities

• Guide research and engineering teams to close knowledge gaps and improve AI model performance in MLOps, training infrastructure, and ML framework-level topics.
• Design challenging, domain-relevant tasks, and write accurate and well-structured solutions to MLOps and ML systems problems.
• Evaluate MLOps tasks and solutions and provide clear, written technical feedback.
• Develop guidelines and detailed rubrics/evaluation frameworks to assess training pipeline design, distributed systems reasoning, and kernel-level optimization across tasks.
• Collaborate with other subject matter experts to ensure consistency and accuracy in training data.

Skills

JAX

PyTorch

GPU kernels

MLOps

ML systems

Communication

Job description

Mercor is seeking an experienced MLOps Engineer to help scale AI research pipelines. The role centers on JAX and PyTorch, focusing on training infrastructure and kernel-level optimization. You will lead complex tasks and provide clear feedback on solutions, collaborating with research and engineering teams.

This remote contract position requires 40 hours/week and strong communication to guide projects, set rubrics, and ensure high-quality model performance across platforms.