AI Summary
Mercor connects elite creative and technical talent with leading AI research labs. This MLOps Engineer Expert role involves guiding research teams, designing domain-relevant tasks, evaluating solutions, and developing evaluation frameworks for AI model performance. Candidates must have 2+ years of ML infrastructure experience, hands-on production with JAX/PyTorch, and custom GPU kernel optimization skills.
Key Highlights
Contract position with $90–$140/hour compensation
40 hours/week commitment
JAX and PyTorch production experience required
Custom GPU kernel optimization with Pallas or Triton required
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
Remote work
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 Expert
Type: Contract
Compensation: $90–$140/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 40 hours/week during weekdays.
- Strong written communication skills and the ability to explain complex technical decisions clearly.
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