AI Summary
MLOps Engineer responsible for guiding research and engineering teams to improve AI model performance in MLOps, training infrastructure, and ML framework-level topics. Requires 2+ years of experience in ML infrastructure, MLOps, or ML systems engineering. Must have hands-on production experience with JAX and/or PyTorch at scale.
Key Highlights
Guide research and engineering teams
Design challenging tasks and write solutions
Evaluate MLOps tasks and solutions
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 and well-structured solutions to MLOps and ML systems problems.
Evaluate MLOps tasks and solutions and provide clear, written technical feedback.
Technical Skills Required
Benefits & Perks
$35-$45/hour
Remote work
40 hours/week
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: MLOps Engineer (JAX, PyTorch, Pallas/Triton) — IN
Type: Contract
Compensation: $35–$45/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 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.
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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 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 30 hours/week during weekdays.
- Strong written communication skills and the ability to explain complex technical decisions clearly.
- W-2 employment with Cincinnatus LLC.
- Paid weekly.
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