Top Tech Jobs & Startup Jobs in Seattle, WA

The Agency Recruiter operates their own full desk, managing client relationships, sourcing candidates, and collaborating with peers to achieve recruitment goals in a commission-based environment.

Serve as a strategic advisor to executive leadership on AI infrastructure, cloud and HPC strategy. Evaluate emerging AI technologies and infrastructure investments, support technical due diligence for partnerships and M&A, develop executive presentations and decision frameworks, and advise on governance, responsible AI, data privacy, and regulatory trends. Collaborate cross-functionally to translate business priorities into actionable technology strategies and assess risks, scalability, and long-term operational economics.

The Principal Applied Scientist will lead research and development in generative and agentic AI, focusing on fraud detection and risk management, collaborating with teams to build scalable AI solutions.

The Senior Applied Scientist will develop and deploy generative AI and deep learning solutions, collaborating with a team to solve real-world challenges.

Develop and deploy generative AI and deep learning solutions for identity verification and fraud prevention, collaborating with scientists and engineers.

Lead design of GPU servers and rack-scale infrastructure for AI workloads, partnering with GPU vendors, ODMs/OEMs, and data center teams. Own mechanical layout, power distribution, thermal design, cable management, validation, and production readiness while balancing performance, reliability, serviceability, and cost. Influence hardware standards and strategy across large-scale deployments.

Optimize GPU kernels and data-plane performance for large-scale AI workloads by profiling, identifying bottlenecks, tuning kernels, developing benchmarks and tooling, and collaborating with ML and platform teams to maximize GPU utilization, throughput, and reliability.

Build and operate production-grade model-serving and inference systems focused on high throughput, low latency, GPU efficiency, scalability, and reliability. Collaborate with GPU performance, training, orchestration, and infrastructure teams to optimize inference performance, design scalable systems, implement monitoring/alerting, and resolve production capacity and reliability issues.

Build and scale distributed training infrastructure for large AI models across GPU clusters. Improve training reliability, efficiency, fault tolerance, checkpointing, and resource utilization. Integrate training systems with production ML pipelines, develop tooling and automation to improve developer experience, diagnose training throughput and stability issues, and establish operational best practices for a growing AI infrastructure platform.

Design, build, and operate GPU-focused virtualization and Kubernetes orchestration systems for multi-tenant AI/HPC clusters. Implement automated provisioning, workload scheduling, resource management, and tooling to scale, secure, and run GPU compute reliably across production environments while partnering with hardware, networking, and platform teams.