About the job
As part of the Systems Planning and Architecture (SPARC) group, you will contribute to research and exploration in advanced machine learning (ML) systems, focusing on the numeric, data types, and compute technologies that drive the next generation of Artificial Intelligence (AI) workloads at Azure scale. You will collaborate across Azure teams to investigate cutting-edge approaches in model efficiency ranging from low-precision formats, quantization strategies, and ML kernel development, to benchmarking and analyzing emerging model architecture and hardware capabilities. As a Research Intern, you will be at the forefront of innovation in cloud-scale AI, helping shape how Microsoft designs and deploys efficient and performant ML infrastructure. Your work will directly inform decisions around compute platforms, acceleration strategies, and system-level optimizations that influence internal silicon efforts, software runtimes, and partnerships across the industry. You will play a critical role in evaluating, prototyping, and analyzing new algorithmic and numerical techniques that improve the performance, cost, and efficiency of training and inference for large-scale models. This Research Internship offers opportunities to develop expertise in ML systems, emerging data types, kernel optimization, and performance modeling while gaining hands-on experience with the latest Azure AI and hardware technologies.
Responsibilities
Research Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world’s best researchers, Research Interns learn, collaborate, and network for life. Research Interns not only advance their own careers, but they also contribute to exciting research and development strides. During the 12-week internship, Research Interns are paired with mentors and expected to collaborate with other Research Interns and researchers, present findings, and contribute to the vibrant life of the community. Research internships are available in all areas of research, and are offered year-round, though they typically begin in the summer.
Qualifications
Minimum
Currently enrolled in a master’s, or PhD program in Computer Science, Electrical Engineering, or a related STEM field.
Completed at least 2 academic courses or projects involving machine learning systems.
At least 3 years of experience programming in Python, C++, or a similar systems-oriented language through work, projects, or research.
Other Requirements
Research Interns are expected to be physically located in their manager’s Microsoft worksite location for the duration of their internship.
In addition to the qualifications below, you’ll need to submit a minimum of two reference letters for this position as well as a cover letter and any relevant work or research samples. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.
Preferred
Demonstrable Contribution to open-source ML framework or ML systems software.
Deep and strong understanding of transformer-based model architectures, including attention mechanisms, KV cache behavior, and common training and inference bottlenecks.
Experience with modern ML frameworks and runtimes such as PyTorch, Hugging Face Transformers, SGLang, vLLM, or TensorRT-LLM.
Experience with GPU or accelerator programming using CUDA, Triton, or similar tools, and familiarity with profiling and performance analysis.
Familiarity with benchmarking and performance profiling tools for ML workloads.
Working knowledge of low-precision numeric, quantization methods, or hardware–software co-design considerations for large-scale model efficiency is a plus.
Coursework, research, or project experience in areas such as ML systems, model optimization, kernel development, or numerical computing.
Proficient analytical and problem-solving skills, with an interest in ML systems and computational performance.