Top AI & Machine Learning Jobs in Seattle, WA

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.

Lead strategic partnerships with AI model developers and cloud technology providers. Identify market opportunities, negotiate complex commercial agreements (OEM, revenue share), and coordinate cross-functional execution with Product, Engineering, Finance, Legal, and GTM teams to expand DigitalOcean's AI‑Native Cloud capabilities.

Lead and grow the Inference Orchestration engineering team to design, build, and operate Kubernetes-based AI infrastructure at scale. Drive scheduling, GPU utilization, topology-aware placement, checkpoint/restore for long jobs, fault tolerance, model distribution, security isolation, and cross-functional delivery to meet performance, cost, and reliability goals.