Top Tech Jobs & Startup Jobs in Seattle, WA

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Designs and executes experiments to characterize integrated quantum systems and hybrid semiconductor-superconductor devices. Analyzes noisy measurement data, communicates actionable findings, collaborates across materials science, microwave engineering, and quantum device physics, and automates measurements using AI where appropriate. The role requires hands-on operation and improvement of complex cryogenic experimental systems, data acquisition, programming, and version control experience.

Designs and delivers EPMS, electrical telemetry, and industrial controls solutions for Microsoft AI datacenters. Responsibilities include defining instrumentation and communications requirements, configuring EPMS hardware and networks, developing system integrations, testing and commissioning technologies, troubleshooting complex electrical and controls issues, producing technical documentation, and coordinating with multidisciplinary engineering, vendor, commissioning, and deployment teams.

Embed with strategic customers to lead end-to-end technical programs, applying AI-assisted engineering to accelerate delivery. Coordinate cross-functional teams, make data-driven decisions, manage risks, and cultivate reusable solution patterns and playbooks to scale impact.

Owns financial leadership and controllership for Microsoft’s Worldwide OEM incentives portfolio. Responsibilities include financial reporting, accounting processes, risk assessment, compliance with GAAP, IFRS, SOX, and company policy, financial modeling, stakeholder partnership, process optimization, and transformation initiatives. The role drives AI-enabled automation, improves controls and reporting, supports regulatory and audit requirements, and executes monthly revenue adjustment accounting.

Owns inventory and material readiness for cryogenic refrigerator upgrades, including tracking stock, staging upgrade kits, and maintaining inventory records. Manages vendor operations such as intake, budgets, contracts, renewals, reporting, and sensitive financial records. Partners with engineering, laboratory, finance, procurement, facilities, and suppliers, with potential growth into cryobay requirements and capital-budget reconciliation.

Design and operate highly available, secure SQL-based Dataverse services at massive scale. Develop data engine features, automation, monitoring, diagnostics, self-healing, and resiliency improvements. Collaborate cross-functionally on SQL data access and authorization, participate in live-site operations, and build scalable data capabilities supporting agentic AI workflows.

Leads global reverse supply chain strategies for Microsoft devices throughout the post-sale lifecycle, including warranty, returns, repair, seedstock, end-of-life, and inventory planning. Owns the supply Plan of Record, inventory targets, excess and obsolete inventory, component buffers, launch readiness, and performance metrics. Partners with repair operations, finance, customer experience, and supply chain teams to improve service, cost, working capital, risk management, and operational scalability.

Lead technical strategy, architecture, and execution for Power BI growth and distribution investments. Design secure, reliable, hyperscale distributed systems across Power BI and Microsoft 365, using telemetry, experimentation, and data-driven decisions to improve adoption and engagement. Partner across engineering, product, design, data science, and business teams; drive architecture reviews, technical standards, operational excellence, mentorship, and cross-team initiatives. Shape personalized analytics platform strategy while balancing scalability, security, privacy, compliance, and maintainability.

Develop, train, optimize, and evaluate statistical and machine learning models using real-world datasets. Prepare and assess data, design controlled experiments, communicate findings, and connect technical metrics to business outcomes. Collaborate with engineering, product, and domain teams to operationalize models, improve AI system performance, and support scalable production deployments.