Senior Staff Engineer, ML Ops (R4941)

Job description

Job responsibilities

• AI Platform Development: Lead the design and implementation of the AI Factory Reference Architecture, delivering a Kubernetes-native platform for AI development, distributed training, simulation, evaluation, and…
• AI Research Enablement: Partner directly with ML researchers to understand evolving training workflows and ensure the platform supports state-of-the-art AI frameworks, foundation model development, reinforcement…
• Developer Experience: Design self-service AI development workflows that enable engineers to move seamlessly from local experimentation to large-scale distributed execution using familiar open source tools and frameworks.
• Distributed AI Infrastructure: Build the infrastructure required to support distributed training, simulation, inference, and reinforcement learning workloads.
• Compute Platform: Design and optimize shared GPU infrastructure across cloud and on-premises environments. Improve resource utilization, scheduling efficiency, storage, networking, observability, and overall platform…
• Data & Model Lifecycle: Build platform capabilities that enable dataset management, experiment tracking, artifact management, model versioning, evaluation, deployment, monitoring, and continuous model improvement.
• Platform Distribution: Develop repeatable deployment and lifecycle management solutions using Infrastructure as Code and modern platform engineering practices.
• Technology Leadership: Evaluate emerging AI infrastructure technologies and establish architectural patterns that balance scalability, performance, maintainability, and developer experience.

Job requirements

• Experience building Kubernetes-native AI or MLOps platforms supporting distributed machine learning workloads.
• Deep understanding of modern AI training frameworks, including PyTorch, Hugging Face Transformers and distributed training techniques.
• Experience operating GPU-accelerated infrastructure and distributed training systems.
• Strong understanding of Kubernetes, Linux, networking, security, storage, and distributed systems.
• Experience with GPU scheduling concepts and large-scale AI workloads.
• Experience packaging and deploying cloud-native infrastructure using Terraform and Helm.
• Strong software engineering skills in Python and Golang and modern cloud-native technologies.
• Experience collaborating closely with ML researchers to translate research workflows into scalable platform capabilities.