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

About The RoleThis 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 EngineerType: W2 | Full-Time Contingent RoleEngagement: Remnote Global | Full-timeCompensation: $90–$140/hourLocation: United States (Remote)Role ResponsibilitiesSupport AI research and engineering teams in improving ML infrastructure and training systemsDesign advanced MLOps and ML systems tasks with accurate, structured technical solutionsEvaluate ML systems outputs and provide detailed technical feedbackDevelop evaluation rubrics and frameworks for distributed systems, training pipelines, and kernel-level optimizationCollaborate with domain experts to maintain consistency and quality across AI training workflowsContribute to improvements in large-scale model training performance and infrastructure reliabilityRequirements2+ years of professional experience in ML infrastructure, MLOps, or ML systems engineeringHands-on production experience with JAX and/or PyTorch at scaleExperience writing or optimizing GPU kernels using Pallas or TritonStrong understanding of ML training systems and distributed infrastructureDemonstrated career progression in engineering or AI infrastructure rolesAbility to commit to a full-time 40-hour/week weekday scheduleStrong written communication and technical documentation skillsEngagement DetailsW2 employment engagementFull-time, 40 hours/weekNo conflicting full-time engagements permittedRemote role within the United StatesOpportunity to contribute to leading frontier AI initiativesApplication & Onboarding ProcessUpload resumeAI interview: A short, 15-minute conversational session to assess background and technical expertiseFollow-up communication with next steps and onboarding details