Role overview
Meta builds technologies that help people connect, find communities, and grow businesses. When Facebook launched in 2004, it changed the way people connect. Apps like Messenger, Instagram and WhatsApp further empowered billions around the world. Now, Meta is moving beyond 2D screens toward immersive experiences like augmented and virtual reality to help build the next evolution in social technology. People who choose to build their careers by building with us at Meta help shape a future that will take us beyond what digital connection makes possible today—beyond the constraints of screens, the limits of distance, and even the rules of physics.
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.
What you'll work on
- 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.
What we're looking for
- 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)