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Machine Learning Ops Engineer | Remote | $90 -$140/hr
Call For ReferralRiverside
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 cut
Machine Learning Ops Engineer | Remote | $90 -$140/hr
Call For ReferralFontanaAbout 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 cut
Machine Learning Ops Engineer | Remote | $90 -$140/hr
Call For ReferralSan BernardinoAbout 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 cut
Machine Learning Ops Engineer | Remote | $90 -$140/hr
Call For ReferralStocktonAbout 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 cut
Machine Learning Ops Engineer | Remote | $90 -$140/hr
Call For ReferralSanta AnaAbout 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 cut
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Machine Learning Ops Engineer | Remote | $90 -$140/hr
- Riverside, California, United States
- Riverside, California, United States
À propos
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
Compétences linguistiques
- English
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