Remote JAX/MLOps Engineer for AI Training Systems

mercor; United States; Remote; USD 96,000 - 152,000; Full time

Job summary

Mercor is seeking a JAX Expert for remote contract work. You will guide research and engineering teams to close knowledge gaps and enhance AI model performance across MLOps, training infrastructure, and ML framework topics. The role requires hands-on JAX experience, GPU kernel optimization, and strong technical communication.

Expect ~40 hours per week with flexible scheduling. This contract position favors candidates with demonstrated career progression and the ability to deliver rigorous

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 at scale.
• Experience writing or optimizing custom GPU kernels using Pallas 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, 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

ML infrastructure

JAX at scale

GPU kernel optimization

Career progression

40 hours/week

Technical communication

Job description

Mercor is seeking a JAX Expert for remote contract work. You will guide research and engineering teams to close knowledge gaps and enhance AI model performance across MLOps, training infrastructure, and ML framework topics. The role requires hands-on JAX experience, GPU kernel optimization, and strong technical communication.

Expect ~40 hours per week with flexible scheduling. This contract position favors candidates with demonstrated career progression and the ability to deliver rigorous