MLOps Engineer | Remote | $70 –$110/hr

Role Overview

MLOps Engineer supporting frontier GenAI initiatives by improving large-scale ML infrastructure, training performance, and distributed training systems.

Responsibilities

• Support AI research and engineering teams in improving ML infrastructure and training systems
• Design advanced MLOps and ML system solutions with structured technical deliverables
• Evaluate ML system 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
• Improve 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 engineering career progression
• Ability to commit to full-time 40 hours/week on weekdays
• Strong written communication and technical documentation skills

Nice to Haves

• (Not explicitly stated; focus is on the kernel-level optimization and distributed training/evaluation expertise.)