AI Infrastructure & Orchestration Platform Lead

DataRobot, Inc.; Washington; Hybrid; USD 180,000 - 240,000; Full time

Benefits offered by this job

Medical, Dental & Vision Insurance

Flexible Time Off

Paid Holidays

Parental Leave

Job summary

DataRobot, Inc. is seeking a senior product leader to own the workload and agent runtime orchestration across Kubernetes-based deployments and accelerators. You will shape the placement, lifecycle, scaling, isolation, and governance of model and agent workloads, partnering with two engineering pods to drive platform evolution.

The role requires deep API thinking, multi-tenancy expertise, and strong technical writing to articulate contracts and roadmaps for scalable AI workloads.

Qualifications

• 6+ years in product management for infrastructure or cloud services.
• 9+ years for principal candidates with a platform layer background.
• Deep technical understanding of GPU and accelerator behavior: topology-aware placement and resource sharing.
• Strong API product judgment and experience drafting API contracts.
• Experience with multi-tenancy, isolation, quotas, and fairness in architectures.
• BS or MS in Computer Science or equivalent hands-on infra experience.

Responsibilities

• Own the workload and agent runtime orchestration: placement, lifecycle, scaling, and isolation on Kubernetes and accelerators.
• Define the API contracts, versioning strategy, and governance for deployment and usage.
• Manage resource placement, capacity planning, and cross-tenant quota controls.
• Collaborate with two engineering pods and align with product teams building on the platform.
• Engage in prototyping, documentation, and roadmap decisions to guide platform evolution.

Skills

Product management

Kubernetes

API design

Platform engineering

Multi-tenancy

Technical writing

Education

BS or MS in Computer Science

Tools

Kubernetes

Docker

Job description

DataRobot, Inc. is seeking a senior product leader to own the workload and agent runtime orchestration across Kubernetes-based deployments and accelerators. You will shape the placement, lifecycle, scaling, isolation, and governance of model and agent workloads, partnering with two engineering pods to drive platform evolution.

The role requires deep API thinking, multi-tenancy expertise, and strong technical writing to articulate contracts and roadmaps for scalable AI workloads.