Senior Technical Product Manager - Serverless AI

About the role

The Senior Technical Product Manager will lead the Serverless AI product team, owning the product roadmap for jobs, endpoints, and devpods. They will write detailed PRDs, make technical trade-off decisions, and shape the CLI experience, pricing, and adoption.

Responsibilities

• Product Ownership: Co-own the Serverless AI product roadmap, take primary ownership of specific product areas, and collaborate with other PMs on shared priorities and cross-cutting decisions.
• Write Detailed PRDs: Create PRDs specifying CLI syntax, API contracts, state machines, and billing models, not abstract feature descriptions.
• Make Build/Build/Defer Decisions: Evaluate technical trade-offs and make decisions on capabilities like autoscaling, multi-node orchestration, HTTPS termination, secret injection, and health checking based on customer signal and strategic priorities.
• Technical Depth: Understand the full workload lifecycle, evaluate technical trade-offs, work directly with engineers on architecture decisions, and stay current on the fast-moving serverless GPU infrastructure space.
• Customer & Market: Run customer discovery and feedback sessions, analyze usage data, define and iterate on pricing, packaging, and tier strategy, and partner with marketing on developer-focused campaigns.
• Go-to-Market: Own the technical content strategy, and work with Solution Architects and Sales to qualify serverless-fit opportunities and support technical evaluations.

Requirements

• Hands-on experience with infrastructure or platform products used by developers or ML engineers.
• Understanding of containers at a practical level, including Docker, image registries, container runtimes, resource limits, networking.
• Working knowledge of GPU computing for AI/ML, including GPU types, training and inference workloads, and inference serving concepts.
• Experience shaping developer-facing APIs, CLIs, or SDKs.
• Experience with Kubernetes for ML workloads (Kubeflow, KServe, Ray Serve).
• Experience building a product from early stage to scale in a fast-growing market.

Qualifications

• 3+ years of product management experience in cloud infrastructure, AI/ML platforms, or developer tools.
• Ability to whiteboard a workload lifecycle and identify failure modes at each step.
• Familiarity with autoscaling trade-offs, inference serving concepts, and distributed training concepts.
• Experience with pricing models and reasoning about their interactions with product architecture.

Skills

• Ability to reason about pricing models, understand autoscaling trade-offs, and reason about distributed training concepts.
• Experience with Kubernetes for ML workloads and understanding of why many ML teams want to avoid it.
• Experience building a product from early stage to scale in a fast-growing market.
• Experience with systems engineering, distributed systems, or site reliability engineering.