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Machine Learning Ops Engineer | Remote | $90 -$140/hr

$90 - $140 per hour

Call For Referral

About The Role

This role focuses on advancing next-generation AI systems through large-scale ML infrastructure, training optimization, and framework-level engineering. The work involves supporting cutting-edge GenAI initiatives, improving model performance, and contributing to highly scalable AI training environments.

Position: MLOps Engineer

Type: W2 | Full-Time Contingent Role

Engagement: Remnote Global | Full-time

Compensation: $90–$140/hour

Location: United States (Remote)

Role Responsibilities

  • Support AI research and engineering teams in improving ML infrastructure and training systems
  • Design advanced MLOps and ML systems tasks with accurate, structured technical solutions
  • Evaluate ML systems outputs and provide detailed technical feedback
  • Develop evaluation rubrics and frameworks for distributed systems, training pipelines, and kernel-level optimization
  • Collaborate with domain experts to maintain consistency and quality across AI training workflows
  • Contribute to improvements in large-scale model training performance and infrastructure reliability

Requirements

  • 2+ years of professional experience in ML infrastructure, MLOps, or ML systems engineering
  • Hands-on production experience with JAX and/or PyTorch at scale
  • Experience writing or optimizing GPU kernels using Pallas or Triton
  • Strong understanding of ML training systems and distributed infrastructure
  • Demonstrated career progression in engineering or AI infrastructure roles
  • Ability to commit to a full-time 40-hour/week weekday schedule
  • Strong written communication and technical documentation skills

Engagement Details

  • W2 employment engagement
  • Full-time, 40 hours/week
  • No conflicting full-time engagements permitted
  • Remote role within the United States
  • Opportunity to contribute to leading frontier AI initiatives

Application & Onboarding Process

  • Upload resume
  • AI interview: A short, 15-minute conversational session to assess background and technical expertise
  • Follow-up communication with next steps and onboarding details
Vacancy posted 8 hours ago
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