Join our Research & Development teams as a PhD Research Scientist intern in 2026 to explore, develop and help productionize high performance model, software & hardware technologies for Meta's mission to build superintelligence.
Our team's mission is to explore, develop and help productionize high performance model, software & hardware technologies for Meta'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 Meta.
Responsibilities
Explore, prototype and productionize highly optimized ML kernels to unlock full potential of current and future accelerators for Meta's AI workloads.
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.
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