Principal Product Manager, AI Infrastructure and Orchestration

DataRobot delivers AI that maximizes impact and minimizes business risk. Our platform and applications integrate into core business processes so teams can develop, deliver, and govern AI at scale. DataRobot empowers practitioners to deliver predictive and generative AI, and enables leaders to secure their AI assets. Organizations worldwide rely on DataRobot for AI that makes sense for their business — today and in the future.  We run agents and models in production for enterprises that cannot move their workloads to a public cloud: regulated industries, sovereign deployments, air-gapped and customer-managed clusters. The control plane is the layer that makes that possible. It deploys the agents, it deploys the models those agents call, and it decides how every workload is placed, scaled, isolated, routed to, and torn down across Kubernetes clusters and heterogeneous accelerators, on our cloud and on the customer's. Agents and models are one deployment problem here. An agent is a long-lived workload with session state and unpredictable fan-out, calling models with wildly uneven cost profiles, and both land on the same finite pool of accelerators. Allocating that pool correctly is the job. You will own this layer as a product. You will spend your time in design reviews, in the API contract, and in production data.

The deployment and workload API. Resource model, lifecycle semantics, versioning, backward compatibility, and the error behavior customers integrate against. Placement and capacity. How workloads land on nodes and accelerators, how quota and priority work across tenants, and what happens under contention. Scaling. Autoscaling signals, cold start and scale-to-zero economics, headroom policy, and the cost-versus-latency trade-off as a customer-facing control. Agent runtime. Where an agent runs and for how long, how it is isolated, how its tool calls execute, and how its state survives a restart or an eviction. Traffic and connectivity. Ingress and routing for model and agent endpoints, request-aware load balancing, tenancy boundaries, and private connectivity into customer networks. Governance and audit. Who deployed what, who invoked it, under which policy and access control, and whether that record survives a customer audit. Metering and packaging. How inference is measured, quota'd, attributed to a tenant, and priced. Reliability. The SLOs, the error budget, and the operational surface a platform engineer uses to diagnose a degraded deployment without opening a ticket with us. You will hold the roadmap for this layer across two engineering pods and align with the product teams building on top of it.

6+ years in product management for infrastructure, developer platforms, or cloud services, at least 3 of them on Kubernetes-based or distributed systems products. Principal candidates bring 9+ years and a platform layer that other product teams built on. Deep technical understanding of GPU and accelerator behavior: topology-aware placement, fractional and time-sliced sharing, MIG, device plugins and the driver and container runtime plumbing underneath them, memory as the binding constraint, and what utilization costs when a tenant holds a GPU it is not saturating. Deep technical understanding of Kubernetes: the API server and scheduler, controllers and CRDs, operators, admission and RBAC, device plugins, resource requests and limits, node pools, and what happens when a pod cannot be scheduled. Multi-tenancy experience: isolation models, noisy neighbors, quota and fairness, and tenancy designs that survive a customer security review. API product judgment. You have owned a public or platform API and lived with the consequences of a contract you shipped. Technical writing and prototyping as your default way to make a case: a doc an engineer will read, a deep dive, a public post, an API reference, or a working prototype. Comfort operating with matrixed