Jobs at Microsoft

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 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.

Manage and coach a team of datacenter trainers to design and deliver technical workforce training, assess learning needs, close operational training gaps, partner with site managers, and align training with availability, safety, and throughput goals across global datacenters.

Design and build evaluation infrastructure for generative AI on large GPU clusters. Develop and tune pretraining software, benchmark NVIDIA and AMD GPU clusters, collaborate with model scientists, implement evaluation methods and inference strategies, and inform compute roadmap and architectural decisions to support Copilot and consumer AI products.

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.

Design and implement algorithms, model architectures, data mixtures, and scaling laws for large-scale foundation model training. Run and oversee large distributed training experiments, collaborate with infrastructure, data, and multimodality teams, and apply rigorous, data-driven ablations to drive flagship training runs and system improvements.

Design and build petabyte-scale multimodal data pipelines and infrastructure (text, audio, images, video). Own and maintain systems like Spark, Ray, and vector databases, partner with pre/post-training teams to improve data recipes through experimentation, and support large-scale dataset storage and processing for AI model training.

Contribute to building and scaling ML development infrastructure for Copilot: design and optimize CI/CD for large-scale training, build developer tools, maintain multi-cloud infrastructure, and deploy/host models for inference and data generation while collaborating with cross-functional teams to improve reliability, performance, and model development workflows.

Design, implement, and optimize distributed pre-training infrastructure for large-scale GPU clusters. Profile, benchmark, and debug compute, memory, networking, and storage bottlenecks; optimize communication libraries and GPU programming stacks; collaborate with hardware and model teams to scale training, ensure reliability, and influence architecture and roadmap.

Design and build compute orchestration and scheduling on Kubernetes and Ray for large-scale AI model training and inference. Tune and benchmark pretraining software on Nvidia GB200 and AMD MIxxx GPU clusters, improve cluster efficiency, enable observability and reliability, collaborate with research and framework teams, and influence architecture and roadmap for Copilot and foundational models.

Develop and iterate post-training methods for large language models: drive data collection, build evaluations, design experiments, and apply reward modeling and RL techniques to improve model capabilities and deployment.

Design, curate, and evaluate high-quality multimodal datasets for foundation models. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; collaborate with Safety, Ethics, and research teams to ensure data quality, privacy, and responsible AI alignment.

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.

Drive product strategy and KPIs for backend networking of ultra-large AI supercomputers. Identify failure points, lead remediations and roadmap changes, and coordinate cross-functional teams including customers, datacenter operators, vendors, and product groups to maximize uptime and AI workload throughput.

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 and implement next-generation networking infrastructure for large-scale AI training on Azure. Lead architecture, write secure high-performance code, create test strategies, perform and coach code reviews, integrate AI tools in the SDLC, and collaborate cross-team to ensure scalability, reliability, observability, and security for distributed HPC/AI systems.

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.

Design, build, and optimize high-performance, low-latency networking infrastructure for large-scale AI training on Azure. Benchmark and improve scalability, reliability, and observability; debug complex network issues; author design documents; act as DRI and participate in on-call operations to restore and maintain service availability.

Design and build monitoring, telemetry, and data pipelines to operate flagship supercomputers at scale. Diagnose and troubleshoot GPU hardware, networking, datacenter and software stack issues, implement systemic mitigations, improve observability, and drive reliability and performance improvements through incident reviews and tooling.