MLOps Engineer - AI Specialist

- United States - original posting ->
Status
Open
Remote policy
Remote
Employment type
Not stated
Salary
Not stated
Categories
AI-ML-Engineer, MLOps-Engineering, ML-Infrastructure-Engineering, ML-Systems-Engineering, Machine-Learning-Engineering, AI-Specialist, Senior-MLOps-Engineer, Staff-MLOps-Engineer, AI-ML-Ops-Engineer, AI-ML-Operations-Engineer
Source
himalayas
First observed
2026-08-13 21:52 UTC
Last seen
2026-08-13 21:52 UTC
Source claims posted
2026-08-13 20:31 UTC
Consecutive misses
0 of 10

What the posting says

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

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

Originally posted on Himalayas

Quality

Completeness: 50%

Not enough history yet to judge honesty signals.

Timeline

  1. *
    #52274 2026-08-13 21:52 UTC
    Published