Top Tech Jobs & Startup Jobs in Seattle, WA

Lead design, implementation, and operation of Anthropic's database platform to support millions of users and research workloads. Architect scalable, multi-cloud storage, caching, and data-movement (migrations, backfills, CDC) systems, optimize performance and cost, and partner with product/research teams. Drive build-vs-buy decisions and lead large infrastructure projects to ensure reliability during rapid growth.

Lead technical direction and hands-on development of an accelerator-agnostic inference runtime. Own architecture, performance, scheduling, memory management, validation (canary/shadow/rollback), and cross-accelerator abstractions. Drive integrations with compilers, build systems, and infra, mentor engineers, and coordinate prioritization across serving, scaling, and accelerator teams to ensure efficient, reliable inference at scale.

Seeking an experienced Senior Business Systems Analyst to lead Workday Financials implementation, design financial data models, develop analytics solutions, and collaborate across teams to enhance financial reporting and processes.

Design, build, and operate Anthropic's managed caching platform: a scalable Redis fleet, client libraries, CDC-driven cache invalidation, multi-cloud deployments, observability, and tooling. Drive technical direction, optimize latency/hit rates/cost, partner with product and research teams, and make build-vs-buy decisions for caching infrastructure.

The Technical Program Manager will coordinate model launches, manage infrastructure integration, optimize performance, and ensure effective communication across teams.

Responsible for recruiting top talent for Core Tech teams, focusing on Machine Learning and AI. Collaborates with hiring managers to improve processes and enhance employer branding while scaling diverse teams.

As a Research Engineer, you will develop large scale ML systems, optimize infrastructure and enhance research tools while ensuring reliability and safety.

Manage operational health and progress of reliability infrastructure in AI systems, driving incident responses, maintaining service-level objectives, and coordinating platform projects.

Build and maintain high-performance distributed inference systems to serve LLMs at scale. Implement intelligent request routing, autoscaling, load balancing, deployment pipelines, and integrate diverse AI accelerators across multi-cloud environments. Use observability to tune production performance and enable research workflows.

Lead architecture, roadmap, and implementation for feature flagging, dynamic configuration, and A/B/experimentation infrastructure. Build scalable distributed systems, libraries, tooling, and APIs/CLI to enable safe rollouts, experiments, and measurement across research and production. Partner with deployment, testing, and internal teams to define standards and improve developer productivity.