Our team's mission is to make Py Torch models high-performing, deterministic and stable, via a robust foundational framework that supports the latest hardware, without sacrificing the flexibility and ease of use of Py Torch.We are seeking a PhD Research Intern to work on next-generation Mixture-of-Experts (MoE) systems for Py Torch, focused on substantially improving end-to-end training and inference throughput on modern accelerators (e.g., NVIDIA Hopper and beyond).This internship will explore novel combinations of communication-aware distributed training and kernel- and IO-aware execution optimizations (inspired by Sonic MoE and related works) to unlock new performance regimes for large-scale sparse models. The project spans systems research, GPU kernel optimization, and framework optimization, with opportunities for open-source contributions and publication.

Team scope:

• Improve Py Torch out-of-the-box performance on GPU, CPU, accelerators- Vertical performance optimization for models for training and inference- Model optimization techniques like quantization for improved efficiency- Improve stability and extensibility of the Py Torch framework Our internships are twelve (12) to twenty-four (24) weeks long and we have various start dates throughout the year.

Research Scientist Intern, Py Torch Framework Performance (PhD) Responsibilities:

• Design and evaluate communication-aware, kernel-aware, and quantization-aware MoE execution strategies, combining ideas such as expert placement, routing, batching, scheduling, and precision selection.
• Develop and optimize GPU kernels and runtime components for MoE workloads, including fused kernels, grouped GEMMs, memory-efficient forward and backward passes.
• Explore quantization techniques (e.g., MXFP8, FP8) in the context of MoE, balancing accuracy, performance, and hardware efficiency.
• Build performance models and benchmarks to analyze compute, memory, communication, and quantization overheads across different sparsity regimes.
• Run experiments on single-node and multi-node GPU systems.
• Collaborate with the open-source community to gather feedback and iterate on the project.
• Contribute to Py Torch (Core, Compile, Distributed) within the scope of the project.
• Improve Py Torch performance in general.

Minimum Qualifications:

• Currently has, or is in the process of obtaining, a PhD degree in the field of Computer Science or a related STEM field
• Deep knowledge of transformer architectures, including attention, feed-forward layers, and Mixture-of-Experts (MoE) models
• Strong background in ML systems research, with domain knowledge in MoE efficiency, such as routing, expert parallelism, communication overheads, and kernel-level optimizations
• Hands-on experience writing GPU kernels using CUDA and/or cuteDSL
• Working knowledge of quantization techniques and their impact on performance and accuracy
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

Preferred Qualifications:

• Experience working on other ML compiler stack, especially on PT2 stack
• Familiarity with distributed training and inference, such as data parallelism and collective communication
• Ability to independently design experiments, analyze complex performance tradeoffs, and clearly communicate technical findings in writing and presentations
• Intent to return to degree program after the completion of the internship/co-op
• Proven track record of achieving significant results as demonstrated by grants, fellowships, patents, as well as first-authored publications at leading workshops or conferences such as NeurIPS, MLSys, ASPLOS, PLDI, CGO, PACT, ICML, or similar
• Experience working and communicating cross functionally in a team environment

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$7,650/month to $12,134/month + benefits
Individual compensation is determined by skills, qualifications, experience, and location. Compensation details listed in this posting reflect the base hourly rate, monthly rate, or annual salary only, and do not include bonus, equity or sales incentives, if applicable. In addition to base compensation, Meta offers benefits. Learn more about benefits at Meta.