AI Summary
Mercor seeks a JAX Expert for a remote contract role to guide AI research and engineering teams. Responsibilities include improving AI model performance, designing and solving MLOps/ML systems problems, and evaluating solutions. Requires 2+ years in ML infrastructure/MLOps and hands-on JAX production experience at scale.
Key Highlights
Guide AI research and engineering teams on MLOps, training infrastructure, and ML framework topics.
Design and solve challenging MLOps and ML systems problems, providing technical feedback.
Requires 2+ years of ML infrastructure/MLOps experience and hands-on JAX production experience at scale.
Key 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.
Technical Skills Required
Benefits & Perks
$70–$110/hour
Remote
Nice to Have
Experience writing or optimizing custom GPU kernels using Pallas or Triton.
Job Description
About The Job
Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.
Position: JAX Expert
Type: Contract
Compensation: $70–$110/hour
Location: Remote
Commitment: 40 hours/week
Role 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.
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Must-Have
- 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.
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