This role is perfect for an engineer who enjoys working close to the metal and across the research boundary. You'll collaborate with researchers and systems architects to bridge algorithmic design with hardware efficiency. You'll prototype new kernel implementations, profile performance across hardware generations, and help define the numerical and parallelism strategies that determine how we scale next-generation AI systems.

Responsibilities

• Design and implement custom ML kernels (e.g., CUDA, CuTe, Triton) for core LLM operations such as attention, matrix multiplication, gating, and normalization, optimized for modern GPU and accelerator architectures.
• Design and think through compute primitives to reduce memory bandwidth bottlenecks and improve kernel compute efficiency.
• Collaborate with research teams to align kernel-level optimizations with model architecture and algorithmic goals.
• Develop and maintain a library of reusable kernels and performance benchmarks that serve as the foundation for internal model training.
• Contribute to infrastructure stability and scalability, ensuring reproducibility, consistency across precision formats, and high utilization of compute resources.
• Document and share insights through internal talks, technical papers, or open-source contributions to strengthen the broader ML systems community.

Skills and Qualifications

Minimum qualifications:

• Bachelor’s degree or equivalent experience in computer science, electrical engineering, statistics, machine learning, physics, robotics, or similar.
• Strong engineering skills, ability to contribute performant, maintainable code and debug in complex codebases
• Understanding of deep learning frameworks (e.g., PyTorch, JAX) and their underlying system architectures.
• Thrive in a highly collaborative environment involving many, different cross-functional partners and subject matter experts.
• A bias for action with a mindset to take initiative to work across different stacks and different teams where you spot the opportunity to make sure something ships.
• Proficiency in CUDA, CuTe, Triton, or other GPU programming frameworks.
• Demonstrated ability to analyze, profile, and optimize compute-intensive workloads.

Preferred qualifications:

• Experience training or supporting large-scale language models with tens of billions of parameters or more.
• Track record of improving research productivity through infrastructure design or process improvements.
• Experience developing or tuning kernels for deep learning frameworks such as PyTorch, JAX, or custom accelerators.
• Familiarity with tensor parallelism, pipeline parallelism, or distributed data processing frameworks.
• Experience implementing low-precision formats (FP8, INT8, block floating point) or contributing to related compiler stacks (e.g., XLA, TVM).
• Contributions to open-source GPU, ML systems, or compiler optimization projects.
• Prior research or engineering experience in numerical optimization, communication-efficient training, or scalable AI infrastructure.

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Microsoft AI are looking for a talented Member of Technical Staff, Infrastructure Data & Analytics to join their MAI SuperIntelligence Team. This role sits at the heart of strategic decision-making, turning raw telemetry into trusted, decision-quality insights on utilization, capacity, readiness, and efficiency. You'll work directly with leadership to shape the company's direction in the Superintelligence space.

About the Role

As a Member of Technical Staff, Infrastructure Data & Analytics, you will act as the technical lead and owner for infrastructure analytics across compute, storage, and networking. You will design and build durable, scalable data pipelines that ingest telemetry from clusters, schedulers, health systems, and capacity trackers into Data Warehouse. You will define and standardize core metrics and semantics (e.g., utilization, occupancy, MFU, goodput, capacity readiness, delivery-to-production). You will architect and maintain self-service dashboards and APIs for fleet, cluster, and squad-level visibility. You will partner closely with stakeholders across Supercomputing Infra, Researchers, Strategy and Executives to ensure metrics reflect operational and business reality.

Accountabilities

• Act as the technical lead and owner for infrastructure analytics across compute, storage, and networking.
• Design and build durable, scalable data pipelines that ingest telemetry from clusters, schedulers, health systems, and capacity trackers into Data Warehouse.

The Candidate we're looking for

Experience:

• 8+ years technical engineering experience with data engineering, analytics, or data science, with increasing technical ownership in startup environment.

Technical skills:

• Distributed data processing frameworks and large-scale data systems.

Personal attributes:

• Strong communication skills; can explain complex systems clearly to senior leader.

Benefits

• Software Engineering IC5 – The typical base pay range for this role across the U.S. is USD $139,900 – $274,800 per year.
• Certain roles may be eligible for benefits and other compensation.

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What you'll do

• Design, build, and sustain efficient, scalable and performant Data Engineering Pipelines to ingest, sanitize, transform (ETL/ELT), and deliver high-volume, high-velocity data from diverse sources.
• Ensure reliable and consistent processing of versatile workloads of granularity such as Real Time, Near Real Time, Mini-batch, Batch and On-demand.

What you need

• Strong Proficiency in writing and analyzing complex SQL, Python or any 4GL.

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What you'll do

• Design, build, and sustain efficient, scalable and performant Data Engineering Pipelines to ingest, sanitize, transform (ETL/ELT), and deliver high-volume, high-velocity data from diverse sources.
• Ensure reliable and consistent processing of versatile workloads of granularity such as Real Time, Near Real Time, Mini-batch, Batch and On-demand.

What you need

• Strong Proficiency in writing and analyzing complex SQL, Python or any 4GL.

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