Top AI & Machine Learning Jobs in Seattle, WA

Build and operate DigitalOcean’s managed agent runtime platform for secure, scalable AI coding agents and agentic workflows. Responsibilities include distributed services, agent framework integrations, control-plane APIs, execution orchestration, sandboxed environments, developer APIs, observability, reliability, and end-to-end feature ownership. The role requires extensive backend engineering experience, AI/LLM integration expertise, distributed systems knowledge, and experience with cloud infrastructure, orchestration, databases, and high-throughput APIs.

Design, deploy, optimize, and scale production AI and agentic workloads for strategic customers on DigitalOcean’s AI-Native Cloud. Build benchmarking systems, deployment frameworks, automation tooling, starter kits, and operational playbooks. Optimize inference performance, GPU efficiency, latency, reliability, and cost across distributed systems. Validate new platform capabilities, influence product roadmaps, collaborate with vendors and partners, and enable customer-facing teams through reference architectures and technical guidance. Travel up to 30% for customer engagements, workshops, conferences, and collaboration.

Own the product strategy, roadmap, and execution for DigitalOcean’s managed agentic AI infrastructure products. Define developer experiences, APIs, CLIs, documentation, pricing, packaging, and launch plans for services supporting long-running agents, tool execution, state management, secure sandboxes, identity, observability, and scale. Partner with customers, engineering, finance, security, legal, design, and go-to-market teams to turn emerging agent infrastructure needs into reliable, commercially successful products.

Owns the end-to-end optimization of large language model inference across NVIDIA and AMD GPU fleets. Responsibilities include quantization, GPU kernel development, attention and MoE optimization, speculative decoding, parallelism strategies, benchmarking, regression infrastructure, and upstream contributions to serving frameworks. The role sets technical direction, partners with hardware vendors, mentors engineers, and drives production enablement for newly released models.

Leads DigitalOcean’s Applied Research function for agentic AI, setting the research agenda across model routing, memory, observability, and reinforcement learning. Builds and manages a research team, connects research investments to product outcomes, and partners with engineering to ship production capabilities that improve reliability, cost, latency, and task success. Represents the company externally through publications, talks, open-source work, and research collaborations.

Design and optimize scalable, multi-tenant serverless inference infrastructure and APIs for large-scale AI workloads. Build highly available distributed services, improve GPU utilization, observability, capacity management, fault tolerance, and operational automation. Partner with platform and GPU infrastructure teams, contribute to architecture decisions, participate in on-call and incident response, and provide technical leadership and mentorship to engineers.

The Staff Forward Deployed Engineer drives AI adoption for strategic customers by solving complex cloud infrastructure challenges, developing scalable assets, and influencing product roadmaps through collaboration and technical expertise.

Lead design and implementation of scalable, multi-tenant serverless inference services focused on throughput, GPU utilization, resiliency, observability, and operational tooling. Partner with platform and GPU teams, debug production performance issues, mentor engineers, drive incident response, and create reusable patterns, standards, and automation for inference workloads.

Lead applied research and engineering for feedback-driven agentic AI: define reward modeling and reinforcement learning roadmap, design production learning loops and evaluation frameworks, run large-scale experiments, and ship research into production while partnering across product and engineering.

Lead multi-year product strategy and roadmap for DigitalOcean's Data and AI platform. Own development of vector databases, knowledge bases, Iceberg-supported data primitives, and integrations for RAG and AI agents. Drive cross-functional execution, pricing, capacity planning, and customer-driven feature prioritization; measure utilization, reliability, and deployment velocity.