Principal Product Manager – Inference Engine

DigitalOcean

. Own the GPU strategy for DigitalOcean's inference business across serverless inference, dedicated inference, batch workloads, and future offerings .

Posted 9/30/2026full-timeSeattle • Washington • United StatesLead💰 $218,000 - $273,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in GPU strategy and infrastructure optimization for inference systems, with a strong focus on GPU economics, developer experience, and operational metrics. Capable of translating customer needs into technical requirements while balancing business goals and technical depth.

Highest-signal resume keywords

GPU EconomicsInference SystemsCloud ServicesAnalytical RigorTechnical Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Infrastructure BuildingML PlatformsModel ServingLatency OptimizationCapacity PlanningCost-to-Serve AnalysisFramework DevelopmentDecision SystemsBatchingPrompt Caching

Soft Skills

Analytical ThinkingCommunicationAdaptabilityCollaboration

Industry Keywords

AI-native StartupsDeveloper PlatformsToken RevenueGross MarginOperating MetricsModel AdoptionMedia ModelsThroughputError RatesPricing Models

Tech Stack

Tools & technologies

Cloud

About the role

Key responsibilities & impact

Own the GPU strategy for DigitalOcean's inference business across serverless inference, dedicated inference, batch workloads, and future offerings
Create frameworks to improve GPU utilization, token revenue per GPU hour, infrastructure efficiency, and gross margin
Define the inference product roadmap with engineering
Prioritize prompt caching, autoscaling, batching, latency optimization, observability, dedicated deployments, compliance features, and media model support
Balance developer experience with latency, availability, throughput, pricing, and cost-to-serve
Define SKUs, pricing models, discounting frameworks, and packaging
Work directly with AI-native startups, mid-market customers, and strategic accounts
Translate customer demand and business goals into infrastructure requirements
Establish and track operating metrics including GPU utilization, token throughput, revenue per GPU hour, latency, error rates, model adoption, margin, retention, and capacity efficiency

Requirements

What you’ll need

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
Familiarity with LLM inference, open-source models, model serving, prompt caching, batching, model routing, media models, latency tradeoffs, and production AI application patterns
Technical depth with business orientation
Strong analytical rigor and ability to build frameworks, models, and decision systems
Ability to communicate complex technical and business decisions clearly to senior leaders, customers, and cross-functional teams
Ability to work effectively in ambiguous, fast-moving environments

Benefits

Comp & perks

Reimbursement for relevant conferences, training, and education
Access to LinkedIn Learning's 10,000+ courses
Employee Assistance Program
Local Employee Meetups
Flexible time off policy
Bonus eligibility based on company and individual performance
Equity compensation for eligible employees, including equity grants upon hire
Option to participate in the Employee Stock Purchase Program