Top Tech Jobs & Startup Jobs in Seattle, WA

Supports security assurance for Microsoft government cloud services by analyzing threats, vulnerabilities, and operational security issues. Helps develop mitigation strategies, conduct security reviews, coordinate incident responses, produce analysis reports, and communicate findings to decision-makers. Contributes to threat modeling, secure software development guidance, security research, and assurance planning while collaborating with security teams, customers, and partners.

Partners with engineering leaders on resource allocation, investment optimization, workforce planning, financial analysis, forecasting, and portfolio health. Builds models and business insights, monitors performance against plan, identifies risks and tradeoffs, and improves financial processes. Serves as a trusted advisor to leadership, influencing expense management and investment decisions through data-driven analysis and effective storytelling.

Develop scalable AI-powered operations and ML infrastructure for reliable, secure cloud systems. Design distributed systems, write maintainable code, improve architecture and operational excellence, mentor engineers, establish engineering standards, and lead testing, incident response, and on-call activities. The role requires Azure or cloud experience, coding proficiency, and expertise in automation, reliability, CI/CD, infrastructure as code, and responsible AI practices.

Leads a software engineering team responsible for Microsoft Purview platform services, including authorization, policies, alerts, audits, and scopes. Owns feature delivery from strategy through customer adoption; drives architecture, reliability, security, compliance, operational excellence, and modernization. Coaches engineers, manages performance and career development, coordinates cross-functional roadmaps, oversees incident readiness and technical debt, and communicates risks and progress to senior leadership.

Designs algorithms and applications for fault-tolerant quantum computers. Develops modeling tools to assess quantum resource requirements across hardware architectures and creates application- and architecture-aware techniques for quantum circuit compilation and optimization. The role involves research and software development, cross-functional collaboration, and advancing quantum applications, platforms, and hybrid quantum-classical workflows.

Build and optimize distributed systems supporting large-scale reinforcement learning, LLM post-training, inference, and production AI workloads. Develop reliable platform services, improve researcher iteration loops, debug model-hardware interactions, optimize performance, and enhance observability, security, scalability, and operational reliability. Participate in architecture, coding, incident response, deployment planning, and on-call support. Required qualifications include a technical bachelor’s degree or equivalent experience and at least two years of software engineering experience.

Lead end-to-end acquisition and delivery of easements for datacenter infrastructure, including power, water, fiber, and access. Manage negotiations, consultants, legal workflows, diligence, risks, milestones, stakeholder communications, and executive reporting. Develop standardized playbooks, templates, and process controls to improve delivery speed, quality, and predictability across a complex, multi-stakeholder portfolio.

Lead design and implementation of scalable, reliable, secure distributed advertising platform services. Drive architecture, partner with PM/Data Science, deliver end-to-end projects, use telemetry and A/B testing, mentor engineers, and influence platform-wide technical direction.

Partner with cross-functional leaders to drive forecasting, budgeting, long-range planning, monetization, and performance reporting. Produce data-driven insights, scalable financial models, and scenario analyses to influence investment decisions, operational efficiency, and executive-level strategy across consumer product portfolios.

Lead development and large-scale training of foundation models across pretraining, fine-tuning, and post-training. Design experiments and evaluations, diagnose training failures, improve model quality, scale methods on GPU clusters, and provide technical direction and mentorship across research and engineering teams.