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Research Scientist Intern, MSL Infra Kernels & Optimizations

United States, Menlo Park 7650.00 - 12134.00 USD / Month · Job Posted January 29, 2026
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Job Description

Meta’s Meta SuperIntelligence Labs (MSL) Infra Kernels & Optimizations (K&O) team is looking to hire PhD Research Scientists interns in 2026 to join our Research & Development teams. Our teams’ mission is to explore, develop and help productionize high performance model, software & hardware technologies for MSL’s mission to build superintelligence. We achieve this via concurrent design and optimization of many aspects of the system from models and runtime all the way to the AI hardware, optimizing across compute, network and storage. The team invests significantly into model optimization on existing accelerator systems and guiding the future of models and AI HW at Meta. This drives improved performance as compute multipliers, new model architectures and reduces cost of ownership for all key AI services at FB: Recommendations and Generative AI. This is an exciting space that spans exploration and productionization, coupled with close collaborations with industry, academia, Meta’s Infrastructure and Product groups. Collaborating closely with TBD Labs, the team's mode of operation is going from ideation and rapid prototyping, all the way to assisting productization of high leverage ideas, working with many partner teams to bring learnings from prototype into production.

Job Responsibility

  • Explore, prototype and productionize highly optimized ML kernels to unlock full potential of current and future accelerators for Meta’s AI workloads. Open source SOTA implementations as applicable
  • Explore, co-design and optimize parallelisms, compute efficiency, distributed training/inference paradigms and algorithms to improve the scalability, efficiency and reliability of inference and large-scale training systems
  • Optimize inference and training communications performance at scale and investigate improvements to algorithms, tooling, and interfaces, working across multiple accelerator types and HPC collective communication libraries such as NCCL, RCCL, UCC and MPI
  • Innovate and co-design novel model architectures for sustained scaling and hardware efficiency during training and inference
  • Benchmark, analyze, model and project the performance of AI workloads against a wide range of what-if scenarios and provide early input to the design of future hardware, models and runtime, giving crucial feedback to the architecture, compiler, kernel, modeling and runtime teams
  • Explore, co-design and productionize model compression techniques such as Quantization, Pruning, Distillation and Sparsity to improve training and inference efficiency
  • Collaborate with AI & Systems Co-design to guide Meta’s AI HW strategy

Requirements

  • Currently has, or is in the process of obtaining, a PhD degree in the field of Computer Science, Computer Vision, Generative AI, NLP, relevant technical field, or equivalent practical experience
  • Must obtain work authorization in country of employment at the time of hire, and maintain ongoing work authorization during employment
  • Specialized experience in one or more of the following areas: Accelerators/GPU architectures, High Performance Computing (HPC), Machine Learning Compilers, Training/Inference ML Systems, Model Compression, Communication Collectives, ML Kernels/Operator optimizations, Machine learning frameworks (e.g. PyTorch) and SW/HW co-design
  • Experience developing AI-System infrastructure or AI algorithms in C/C++ or Python

Nice to have

  • Intent to return to degree-program after the completion of the internship/co-op
  • Experience or knowledge of training/inference of large scale deep learning models
  • Experience or knowledge of either Generative AI models such as LLMs/LDMs or Ranking & Recommendation models such as DLRM or equivalent
  • Experience or knowledge of distributed ML systems and algorithm development
  • Experience or knowledge of at least one of the responsibilities listed in this job posting
  • Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences
  • Experience working and communicating cross functionally in a team environment
  • Demonstrated software engineer experience via an internship, work experience, coding competitions, or widely used contributions in open source repositories (e.g. GitHub)
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