Jobs at Microsoft

Build and optimize inference systems and tools for generative AI models, collaborate with researchers to improve performance, profiling, debugging, and deploy large-scale distributed inference solutions.

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 infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Lead hardware-software co-design for datacenter-scale AI systems, analyzing real workloads to drive architecture, performance, and cost improvements; prototype and productionize optimizations across accelerators, runtimes, compilers, interconnects, and distributed training/inference; mentor engineers and influence hardware and platform roadmaps.

Lead research on AI methods for life sciences: design, implement, and evaluate novel ML techniques (post-training, inference-time optimization, interpretability, experimental design); benchmark and interpret model capabilities on biological data; optimize inference interactions; and contribute to team-based reproducible research and communication.

Design and develop multimodal foundation-model algorithms and architectures, run large-scale experiments, improve training and deployment efficiency, build datasets and pipelines, perform rigorous evaluation and ablation studies, and collaborate with infrastructure, data, and product teams to responsibly advance AI systems.

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.

Design, build, and operate reliable, scalable services across the C+E stack. Automate operations with scripts and CI/CD, use telemetry to detect trends, perform root cause analysis, support dedicated cloud environments, and collaborate across teams to ensure security, compliance, and operational excellence.

Design, implement, and optimize low-level software for Azure HPC/AI VMs. Diagnose HW/SW integration and performance issues, drive cross-team solutions, maintain service stability, and develop operational playbooks.

Operate and improve large-scale HPC GPU clusters for AI model training and inference. Build observability, automation, CI/CD, and incident response tooling; lead on-call rotations, ensure security/compliance, collaborate with ML engineers, and optimize capacity and costs for production ML workloads.

Design, develop, and operate monitoring and observability solutions for flagship supercomputers. Build data pipelines to process large telemetry and logs, respond to incidents, implement mitigations, write postmortems, and improve reliability, performance, and operational runbooks at scale.

Lead strategic, executive-level engagements to identify, qualify, and accelerate cloud and AI sales opportunities. Drive cross-organizational partnerships, whitespace analysis, AI-driven market intelligence, and forecasting. Architect solution and partner strategies, ensure pipeline hygiene, and align security-focused, AI- and cloud-driven transformations to customer success and regional/global initiatives.

Lead enterprise business transformation using AI (Dynamics 365, Copilot, AI Agents). Define strategy, design Agentic ERP/CRM use cases, build ROI and business cases, engage CxOs, drive large deal creation and cross-functional delivery, and share knowledge internally and externally.

Design, build, and maintain secure, scalable cloud services for air-gapped and sovereign Azure AI workloads. Implement automation, observability, CI/CD, and operations tooling; participate in on-call, incident response, and root cause analysis. Collaborate across teams and use AI-assisted development to accelerate delivery.

Serve as the primary Azure engineering contact for customers: resolve complex incidents, lead escalations, perform deep root-cause analyses, optimize customer environments, synthesize feedback into product improvements, and coordinate across field and engineering teams to improve reliability and customer experience.

Lead end-to-end physical design for large SoC blocks from RTL to GDS, including synthesis, floorplanning, place-and-route, timing signoff, physical verification, EM/IR, formal equivalence, CTS and low-power verification. Drive methodology and tool improvements, coordinate cross-functional teams (CAD, RTL, DFT, architecture, power/performance, foundry), and deliver data-driven optimizations to meet performance, power, area, quality, and schedule goals.

12-month internship supporting Microsoft customers alongside experienced engineers. Learn troubleshooting, diagnose and resolve technical issues, collaborate on projects, join trainings, and develop system administration, networking, virtualization, cloud, and security skills.

Design, implement, and operate cloud-scale telemetry platforms and data pipelines for Azure Monitor. Lead cross-team architecture, mentor engineers, improve AI-enabled development practices, ensure security/compliance, and resolve complex production incidents.

Lead architecture strategy for improving efficiency and reducing costs across Microsoft Copilot Tuning and Frontier Tuning. Drive cross-stack investments spanning model training, inference, serving, orchestration, data pipelines, evaluation, capacity planning, and infrastructure. Establish efficiency metrics, guide quantitative tradeoffs, influence senior leaders and multiple organizations, define execution plans, document architecture practices, and mentor technical talent across frontier-scale AI systems.

Provide technical support by owning, investigating, and resolving customer issues; collaborate across teams, use troubleshooting tools, contribute to readiness and knowledge sharing, and escalate product defects to improve Microsoft products and support processes.