Senior Staff Engineer ML Ops

You will lead the architecture and implementation of an AI platform for distributed training, simulation, evaluation, and deployment. You will build GPU infrastructure and model-lifecycle capabilities, create self-service workflows, package deployments with infrastructure as code, and work with researchers to scale AI workflows.

Responsibilities

• Lead the design and implementation of a Kubernetes-native AI platform
• Partner with ML researchers to support training workflows and AI frameworks
• Design self-service AI development workflows
• Build infrastructure for distributed training, simulation, inference, and reinforcement learning
• Design and optimize GPU infrastructure across cloud and on-premises environments
• Build dataset, experiment, artifact, model-versioning, evaluation, deployment, and monitoring capabilities
• Develop repeatable deployment and lifecycle management solutions using infrastructure as code
• Evaluate AI infrastructure technologies and establish architectural patterns
• Collaborate with researchers, autonomy teams, infrastructure engineers, and product teams

Requirements

• Experience building Kubernetes-native AI or MLOps platforms for distributed machine-learning workloads
• Understanding of PyTorch, Hugging Face Transformers, and distributed training
• Experience operating GPU-accelerated infrastructure and distributed training systems
• Understanding of Kubernetes, Linux, networking, security, storage, and distributed systems
• Experience with GPU scheduling and large-scale AI workloads
• Experience using Terraform and Helm for cloud-native infrastructure
• Software engineering skills in Python and Golang
• Experience translating ML research workflows into scalable platform capabilities

Benefits

• Bonus
• Benefits
• Equity
• Temporary benefits package after 60 days of employment