Senior Machine Learning Engineer

Senior MLOps / ML Infrastructure Engineer Contract: 6 months on W2 Location: Remote About the Role: We are seeking a Senior MLOps / ML Infrastructure Engineer to join our core platform team enabling research and engineering via shared ML systems. This role focuses on building scalable, efficient, and standardized ML workflows that accelerate experimentation and deployment across the organization. Responsibilities: • Design, develop, and maintain scalable ML workflows and pipelines • Build and improve ML infrastructure for training and serving, including GKE-based systems • Automate ML workflows to improve efficiency and reduce operational overhead • Develop robust data sampling and feature generation platforms • Standardize ML training, deployment, and knowledge distillation pipelines • Collaborate closely with researchers and engineers to support large-scale ML experimentation • Drive foundational ML platform tooling and adoption Key Projects / Initiatives: • Scalable ML workflows and pipelines for large-scale ML systems • Automation of end-to-end ML workflows • GKE-based training and serving infrastructure • Knowledge distillation and foundational training tool development Team Overview: • The platform team empowers researchers and engineers with shared ML systems and platforms • Responsible for scalable ML workflows, robust infra, automation, and standardized deployment pipelines Qualifications: Must-have (Technical & Soft Skills): • 5-10+ years of experience in large-scale ML systems, MLOps, or ML infrastructure • Strong expertise in ML workflows, distributed systems, and pipeline automation • Experience with GKE and scalable ML training/serving platforms • Collaborative, ownership-driven, and pragmatic approach to problem-solving • Strong communication and teamwork skills to work with research and engineering teams Preferred Background / Industries: • Experience at large-scale ML/AI companies (Google, Meta, Amazon, Microsoft) • Hands-on MLE or ML infra experience, not purely theoretical or pure DevOps Desired Attributes / Work Style: • Reliability, consistency, and scalability mindset • Cost-aware and methodical in approach • Fast-moving and proactive with strong collaboration skills Success Metrics / KPIs: • Faster time-to-market for ML experiments • Improved training efficiency and infra uptime • Pipeline reliability and cost optimization • Stable deployments and high platform adoption • Reduced onboarding time for ML workflows