Top Tech Jobs & Startup Jobs in Seattle, WA

Lead architecture and development of large-scale VM management and monitoring platforms for Azure. Provide technical leadership, design documentation, debugging, performance tuning, operational excellence, mentoring, on-call monitoring, and adoption of AI-assisted workflows to improve reliability, availability, security, and observability across Windows and Linux environments.

Manage escalated customer and partner issues end-to-end: own incidents, coordinate engineering/operations, communicate status to stakeholders, drive resolution, identify trends, mentor team members, and lead process and vendor-improvement initiatives to enhance support experience and satisfaction.

Customer-embedded principal data scientist who leads technical delivery for strategic accounts, builds production-grade solutions, translates business needs into technical approaches, influences senior stakeholders, and transitions work to FDE teams while operating in ambiguous, fast-paced environments.

Serve as a customer-embedded technical leader aligned to a strategic account, driving business outcomes by building and shipping production-grade solutions, translating business needs into technical approaches, influencing senior stakeholders, preparing transitions to FDE crews, and operating with speed and entrepreneurial ownership in ambiguous, customer-facing environments.

Design, deploy, operate, and tune large-scale HPC environments for AI model training. Own HPC domains (GPU compute, storage, networking), manage schedulers (SLURM, Kubernetes), build automation in Bash/Python, troubleshoot cluster performance, and partner with researchers to support high-scale training workloads.

Design, implement, and optimize high-performance, low-latency networking solutions for large-scale AI training on Azure. Benchmark, debug, and scale distributed systems, create design documents, write and refactor code, act as DRI including on-call operations, and improve observability, reliability, and performance for AI supercomputing infrastructure.

Design, implement, and optimize large-scale distributed training infrastructure and telemetry for GPU clusters. Profile, benchmark, and debug performance across compute, memory, networking, and storage. Improve collective communication libraries and collaborate with ML researchers and hardware teams to scale models, optimize accelerators, and deliver fleet-wide efficiency improvements and automated recommendations.

Design, build, benchmark, and operate large-scale reinforcement learning and pretraining systems on NVIDIA and AMD GPU clusters. Optimize distributed, parallel software for training models, gather compute-roadmap data, and collaborate to deliver production-grade conversational and generative AI features.

Lead Azure pre-sales for strategic enterprise accounts: drive pipeline, forecast, design solutions, run POCs, integrate partners, mentor junior staff, and promote Azure AI and data platforms to accelerate customer cloud adoption.

Design and lead end-to-end enterprise data and GenAI architectures, drive cloud migration/modernization, define GenAI/Copilot/agentic patterns (RAG, embeddings, vector search), establish MLOps/LLMOps and governance, ensure Responsible AI and security, and act as trusted technical advisor across presales through production.