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Plan and optimize GPU capacity for DigitalOcean’s Serverless Instance offering. Analyze demand and utilization, allocate GPU servers across models, forecast infrastructure needs, and automate assignment decisions. Track rack and power capacity, coordinate physical deployments with data center, network, product, and delivery teams, optimize hardware placement, build dashboards, and escalate capacity risks before they affect availability or revenue.

Build automation and internal tools for DigitalOcean’s physical infrastructure using Go, Python, and AI-assisted development. Partner with technical program managers and infrastructure teams to identify and eliminate repetitive manual work, improve hardware and capacity visibility, and support hardware lifecycle engineering. Take automation workflows from discovery through reliable deployment, simplify complex systems, demonstrate work to engineering teams, and contribute to cross-functional infrastructure projects in a hybrid, asynchronous environment.

Supports sustaining engineering for DigitalOcean’s server fleet, including hardware, firmware, cabling, and networking throughout their operational lifecycle. Responsibilities include monitoring and resolving Jira issues, providing Tier 2 escalation support, participating in 24/7 on-call rotation, developing operational standards, creating tooling and runbooks, coordinating monitoring and alerting, troubleshooting failures, and collaborating with infrastructure, firmware, qualification, and operations teams.

Leads electrical, mechanical, controls, and infrastructure engineering for AI and cloud data centers. Develops engineering standards for GPU facilities and liquid cooling, oversees new builds and retrofits, performs colocation due diligence, and supports operations, construction, deployment, and design teams. Owns infrastructure specifications, documentation, and governance while driving reliability, scalability, compliance, operational excellence, talent development, and succession planning across the engineering organization.

Deploys, maintains, and troubleshoots physical data center infrastructure, including servers, networking equipment, storage hardware, power distribution units, and Linux systems. Responsibilities include hardware installation, repairs, upgrades, inventory and asset management, RMA processing, remote work coordination, and shipping equipment. The role is onsite, supports the EAT region, requires on-call participation, and involves 10–15% domestic and international travel.

Assess product and feature security risks through threat modeling, secure architecture reviews, and vulnerability remediation guidance. Partner with product managers, designers, and engineers across the technology stack; promote security culture through training, mentoring, and internal security activities; oversee security debt and explain vulnerability impacts. Build security tooling, automate security practices, develop secure coding patterns, and integrate security tools into engineering workflows.

Assess product and application security through threat modeling, secure architecture reviews, and vulnerability risk assessments. Partner with product managers, designers, and engineers to recommend secure designs, provide remediation guidance, and promote security best practices. Champion security culture through training and mentoring, support vulnerability management, and explain security events. Build security tooling, automation, patterns, and workflows that scale product security practices across engineering.

Assess product and software architecture for security risks, develop threat models, guide vulnerability remediation, and promote secure design practices. Partner with product and engineering teams across the technology stack, provide security training and mentorship, oversee security debt, and build tooling and automation that integrate security into development workflows.

Design and maintain high-throughput backend services and concurrent asynchronous workflows for AI agent evaluation. Build control-plane integrations with secure execution environments, scalable gRPC and REST APIs, and resilient event-driven systems. Collaborate across teams, document technical designs, review code, test comprehensively, monitor performance, investigate bottlenecks, and ensure reliable distributed orchestration. Apply understanding of LLM APIs, prompt constraints, and non-deterministic AI system testing.

The Staff AI Product Manager will drive product strategy for GPU offerings, conduct market analysis, collaborate with teams for product development, and advocate for customer needs. Responsible for managing product lifecycle and utilizing data-driven approaches for product success.