Pre-Training Infrastructure

Microsoft•,

1d•$142,800 - $331,200•Onsite

About The Position

Microsoft AI is seeking a Member of Technical Staff to build and optimize the infrastructure used to train frontier-scale AI models and power Copilot and other consumer products. The role contributes to a fast-moving codebase supporting training across massive GPU clusters, high-throughput storage systems, and cutting-edge AI research. Working closely with model scientists, the position scales research recipes, implements distributed-training parallelism, and improves reliability and performance across thousands of GPUs. The work spans profiling, benchmarking, debugging, and fine-grained optimization across compute, memory, networking, and storage. As part of the core engineering group, the employee will bring engineering best practices, drive architectural changes, and influence the roadmap for relevant software and hardware components. The role also contributes to AI model development and requires collaboration across engineering, research, and hardware teams in a fast-paced, design-driven product environment. Microsoft AI is a startup-like organization within Microsoft focused on advancing Copilot, Bing, Edge, and generative AI research while maintaining commitments to safety, responsibility, and human values. Starting January 26, 2026, employees are expected to work from a designated Microsoft office at least four days a week if they live within 50 miles of that office in the U.S. or 25 miles outside the U.S., subject to local law and jurisdictional variation.

Requirements

Bachelor's degree in Computer Science or a related technical field and 6+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Experience in distributed computing and large-scale systems.
Experience with GPU programming, including CUDA or NCCL, and frameworks such as PyTorch.
Ability to profile, benchmark, and optimize performance-critical systems.
Experience leading technical projects and supporting architectural decisions with data.
Experience building infrastructure for large-scale machine learning or generative AI workloads.
Experience in networking such as InfiniBand or NVLink, storage systems, or distributed training parallelisms.
Track record of contributing to high-performance computing or large-scale AI infrastructure projects.

Nice To Haves

Master's degree in Computer Science or a related technical field and 8+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Bachelor's degree in Computer Science or a related technical field and 12+ years of technical engineering experience coding in languages such as C, C++, C#, Java, JavaScript, or Python, or equivalent experience.

Responsibilities

Design, implement, test, and optimize distributed training infrastructure in Python and C++ for large-scale GPU clusters.
Profile, benchmark, and debug performance bottlenecks across compute, memory, networking, and storage subsystems.
Optimize collective communication libraries such as NCCL for emerging NVLink and InfiniBand topologies.
Collaborate with hardware teams to optimize for next-generation accelerators, including NVIDIA and AMD.
Gather data and insights to develop the pretraining compute roadmap.
Care about conversational AI and its deployment.
Contribute to developing AI models that power Microsoft products.
Find solutions to overcome roadblocks and deliver work to users quickly and iteratively.
Work in a fast-paced, design-driven product development cycle.
Embody Microsoft's Culture and Values.
Bring engineering best practices, drive architectural changes, and influence the roadmap for relevant software and hardware components.