Key Facts

Remote From:

New York (USA)

Category: AI Specialist

Part time

English

Hard Skills

Mload JAX-WS PyTorch (Machine Learning Library) GPU Optimization MLOps (Machine Learning Operations)

Other Skills

•
Communication

Roles & Responsibilities

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.

Requirements:

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.

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.

Qualifications

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.

Application Process (Takes 20–30 mins to complete)

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Resources & Support

For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.

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