Jobs at Microsoft

Lead development of scalable stress, validation, and performance tooling for MAIA AI accelerator platforms. Build workload generation, kernel optimization, compiler/SDK integration, automated orchestration and telemetry, platform validation suites, benchmarking, and CI-driven regression infrastructure to enable platform bring-up, reliability, and performance optimization across hardware and software stacks.

Design, build, test, and deploy integration, diagnostic, and automation tools and agents for DPU-based platforms. Lead test planning and execution, manage field incidents, maintain dashboards and KPIs, drive cross-functional debugging, and feed learnings back into engineering to improve reliability and releases.

Lead integration of cutting-edge RL and agent self-improvement techniques into the Foundry Agent Platform. Design experiments, evaluate and optimize agent performance, collaborate with researchers and engineers, build actionable observability and visualizations, and mentor team members to drive production-quality AI features.

Lead research on agentic systems, agent ecosystems, advanced agentic capabilities, human-agent interaction, evaluation tooling, and responsible AI. Build and deploy prototypes, collaborate across teams, publish research, and translate innovations into practical, impactful applications.

Lead design and delivery of AI-based systems integrating OpenAI technology across Microsoft. Build, ship, and maintain reliable, observable, and secure software services; collaborate with cross-organizational technical partners; navigate business and technical constraints; write technical briefs; identify and escalate risks to deliver software on schedule.

Design and measure metrics, run experiments, analyze results, and build data-driven solutions for Microsoft Advertising. Partner with engineering, product, and stakeholders to iterate product improvements, translate strategy into measurable plans, and communicate actionable insights to influence decisions.

Lead end-to-end physical design from RTL to GDS for flagship silicon: synthesis, floorplanning, place-and-route, timing signoff, physical verification, EMIR, formal equivalence, and low-power verification. Coordinate across CAD, RTL/DFT, architecture, and power teams to optimize PPA, drive design flows and tooling, mentor engineers, and ensure delivery of production tape-outs.

Lead development and operationalization of marketing measurement strategy and systems. Design attribution, incrementality, and MMM approaches; build privacy-first, scalable data pipelines and analytics; drive cross-functional adoption, governance, and executive-ready insights to influence marketing investment and optimization.

Develop machine learning techniques focused on AI safety, alignment, and trustworthiness. Collaborate with product, research, and engineering teams to design, build, deploy, monitor, and iterate responsible AI systems at scale, from experimentation to production for large language models and distributed AI services.

Lead architecture, evaluation, and optimization of high-bandwidth, low-latency networks for AI accelerator platforms. Collaborate with silicon and platform teams to co-design scalable, reliable infrastructure, prototype new network architectures, and influence industry standards and vendor roadmaps to drive Azure AI system performance and TCO.

Lead positioning, go-to-market, and launches for Microsoft Foundry Control Plane security and governance features. Partner with product, engineering, security, and sales to craft messaging, enable field teams, manage analyst relations and partners, and measure adoption and campaign performance.

Lead product marketing for Microsoft AI Content: craft external narratives, define AI quality standards and guardrails, own audience and format strategies globally, partner with Sales to translate capabilities into revenue narratives, and serve as a cross-functional steward aligning Product, Policy, Monetization, and Leadership.

Design, build, and scale cloud-native distributed systems and runtimes to deploy, run, fine-tune, and observe enterprise AI agents. Drive architecture, optimize model/infrastructure interactions, improve developer iteration tooling, enforce secure-by-design practices, and mentor engineering teams to deliver production-ready agent platforms.

Lead vision, strategy, and roadmap for AI-driven creative advertising products. Partner with Data Science, Engineering, UX, Marketing, Legal, and Finance to build generative-asset pipelines, creative automation, quality systems, and go-to-market plans that drive advertiser performance, scale creative production, and protect brand integrity.

Build and scale cloud-native distributed systems for deploying, running, fine-tuning, observing, and optimizing enterprise AI agents. Design reliable, secure platform services, improve developer workflows and tooling, debug model‑infrastructure interactions, and collaborate across CoreAI and Azure to deliver production-ready capabilities.

Lead ecosystem-wide measurement and analytics for Microsoft AI: define metrics, design experiments, apply ML and statistical modeling, mentor data scientists, partner with product and engineering, and standardize data quality and model operationalization to drive product and marketplace improvements.

Design and train large-scale transformer-based models and end-to-end ML systems for ad interaction and conversion prediction. Build weak-label and attribution pipelines, develop multi-task and calibration methods, run offline and online evaluation, and deploy production-ready models that improve ad ranking, bidding, and advertiser ROI at web scale.

Build and maintain cloud-native, large-scale services for deploying, running, fine-tuning, and observing AI agents. Contribute to distributed systems, backend services, tooling, debugging, design discussions, and delivery of production-ready features with strong testing and reliability practices.

Lead AI strategy and operations for frontier model development: produce competitive intelligence, shape operating cadence, allocate compute/resources, coordinate cross-functional execution, and author strategic memos for leadership.

Lead applied research projects that move LLM-based code models from research to product: build and train code models, create datasets from public and internal code, run large-scale ML experiments, collaborate with product and engineering teams, and evaluate and iterate AI for developer tools.