Principal Product Manager - Inference Engine

DigitalOcean•Seattle, WA

5d•$218,000 - $273,000•Hybrid

About The Position

DigitalOcean is seeking a Principal Product Manager for its Inference Engine business. This role is responsible for defining and owning the product strategy for DigitalOcean's inference offerings, with a focus on shaping GPU strategy, pricing, packaging, utilization frameworks, and the product roadmap. The ideal candidate will be a product leader capable of integrating product strategy, technical judgment, and business economics to serve developers and AI-native companies with high-performance inference at scale. This is a significant opportunity to contribute to a high-growth infrastructure business.

Requirements

Deep product judgment in infrastructure or AI, with experience building infrastructure, developer platforms, ML platforms, inference systems, cloud services, or highly technical products for developers and enterprises.
Strong understanding of GPU economics, including utilization, throughput, latency, CapEx, cost-to-serve, gross margin, capacity planning, and workload placement.
Fluency in modern AI workloads, including LLM inference, open-source models, model serving, prompt caching, batching, model routing, media models, latency tradeoffs, and production AI application patterns.
Technical depth with a business orientation, enabling credible work with infrastructure engineers and clear product/business tradeoffs for executives, GTM teams, and customers.
Strong analytical rigor, with the ability to build frameworks and models to guide product direction.
Customer obsession, with a focus on developers and AI-native companies, and an understanding of the need for reliable, performant, simple, and economically sustainable infrastructure products.
Executive communication skills, with the ability to explain complex technical and business decisions clearly.
Ownership mindset, thriving in ambiguous, fast-moving environments.

Responsibilities

Own the GPU strategy for the inference business, including deployment, allocation, pricing, and optimization across various inference offerings.
Maximize GPU utilization and margin by creating frameworks for improving revenue per GPU hour, reducing idle capacity, and reclaiming underutilized infrastructure.
Define the inference product roadmap, prioritizing capabilities such as prompt caching, autoscaling, batching, latency optimization, observability, dedicated deployments, compliance features, and media model support in partnership with engineering.
Balance developer experience with infrastructure economics, building simple-to-use products while making rigorous tradeoffs.
Create pricing and packaging strategies for serverless, dedicated, and enterprise inference customers in collaboration with finance, GTM, and engineering.
Drive customer-backed product decisions by engaging directly with AI-native startups, mid-market customers, and strategic accounts.
Partner deeply with engineering and infrastructure teams to translate customer demand and business goals into infrastructure requirements.
Establish and track key operating metrics for the business, including GPU utilization, token throughput, revenue per GPU hour, latency, error rates, model adoption, margin, customer retention, and capacity efficiency.