Jobs at Microsoft

Lead customer-facing data science engagements: manage data acquisition/cleaning, develop and evaluate scalable models, drive modeling best practices, contribute thought leadership, write extensible code, and define solution and business strategy with cross-functional teams.

Lead customer-facing data science engagements: acquire and prepare data, evaluate and operationalize models at scale, drive modeling best practices, research industry trends, write extensible code, and define solution and customer strategy while promoting ethics and privacy.

Lead cross-functional engineering programs that build cloud and AI solutions with customers. Define success criteria, coordinate roadmaps, track schedules, drive staging/implementation, validate use cases, write test cases, and lead governance and customer engagements.

Drive complex, cross-functional engineering projects as a Product Owner: define customer scenarios, coordinate engineering and stakeholders, deliver roadmaps, track schedules, validate use cases, write test cases, collect metrics, lead governance and customer engagements to implement cloud and AI-based solutions.

Lead and coach a high-performing AI-focused sales team to meet targets, drive solution sales and pipeline, deploy customer solutions, share best practices, partner with stakeholders, and align execution with corporate priorities to accelerate AI transformation and customer impact.

Lead end-to-end segmentation strategy for Microsoft Advertising, translating business priorities into coverage, capacity, and resource-allocation models. Build measurement frameworks, analytical models, and cross-functional alignment to operationalize segmentation into territory design, quota and capacity planning. Advise senior leaders and continuously refine models using data-driven insights to drive growth.

Generate actionable insights for Microsoft Advertising by integrating and analyzing large datasets, applying statistical methods, ensuring data governance, collaborating with internal and client teams, and presenting persuasive visualizations and narratives to influence senior stakeholders and drive ad investment decisions.

Lead global launch operations for Microsoft Advertising, governing end-to-end GTM processes, cross-functional alignment, product and seller readiness, risk mitigation, and program execution. Manage teams, stakeholders, and launch governance to ensure compliant, scalable launches that support order-to-cash accuracy, revenue recognition, and operational readiness at scale.

Lead design of scalable regulatory, governance, and compliance frameworks for advertising; partner across product, engineering, legal, trust & safety, and ops; identify emerging regulatory risks; drive cross-organizational programs balancing customer experience and risk; create intake, prioritization, and readiness processes; produce executive narratives and strategic roadmaps; advise senior stakeholders and build global partnerships to ensure scalable, compliant solutions.

Lead design and delivery of cloud-based solutions, collaborate with customers and product teams, produce extensible code, mentor engineers, own design documents and dependencies, act as a designated responsible individual (DRI) including on-call system monitoring, and improve reliability, performance, and observability at scale.

Work with customers and cross-functional teams to design, build, and deliver cloud-based solutions. Develop, optimize, debug, and refactor code; produce design docs; act as DRI and on-call responder; improve system reliability, observability, and performance; contribute to open-source and collaborate with Microsoft product teams.

Work with customers and cross-functional teams to design, implement, and deliver cloud-based software solutions. Write and reuse code, create design documents, break down work, act as on-call DRI for simple incidents, and stay current with technologies. Contribute to open-source and collaborate with product teams to build scalable, observable systems.

Lead and coach specialized sales resources to drive digital transformation, close new business, and build upsell/cross-sell engines. Orchestrate deal closure, align internal stakeholders, enforce forecasting, and ensure delivery success across assigned territory. Provide technical subject-matter expertise and competitive insight to inform pursuit decisions.

Design, develop, and optimize high-performance, low-latency networking infrastructure for large-scale AI training on Azure. Benchmark, debug, and improve scalability, reliability, observability; author design documents; act as DRI and participate in on-call operations.

Design, build, deploy, and operate AI-native systems and LLM-powered applications for Azure capacity orchestration. Lead architecture and design for distributed systems, write and review production-quality code, drive automation and safe deployments, integrate monitoring and on-call reliability practices, and ensure AI safety and security. Collaborate across teams and mentor engineers while shipping scalable, secure solutions.

Lead consultative, value-driven enterprise sales to plan, orchestrate, and deliver AI Workforce solutions (Copilot, Windows 365, Viva, Agents). Build stakeholder strategies, drive pipeline, cross-sell/up-sell, and partner with internal teams to scale AI adoption and measurable business outcomes.

Design, build, and maintain Azure-based full-stack skilling platforms and CI/CD pipelines. Implement scalable microservices, REST APIs, automation, and AI/LLM features, improve site reliability, automate testing, and collaborate across teams to ensure secure, high-quality production deployments.

Design and lead development of Azure platform capabilities and services. Partner with stakeholders to define requirements, produce design documents, mentor engineers, ensure reliability/observability at scale, serve as DRI and on-call, and drive consistency across distributed systems and lifecycle management.

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 data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.