Job Details

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