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

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