The Collective Compute Enablement team is dedicated to maximizing training performance of Generative AI and Recommendation models on Meta's Training and Inference Accelerator (MTIA). We model and project the performance of current and future training workloads on MTIA while it is being designed to provide early, crucial feedback to the architecture, compiler, and kernels teams. We employ cutting-edge optimization and data parallelization strategies to maximize training throughput for the next generations of LLMs and deep recommendation models, and we work cross-functionally with many partner teams to assure the end-to-end performance of large-scale training in order to more quickly deliver the next generation of AI experiences to our users.

• Apply state-of-the-art AI infrastructure and software/hardware acceleration techniques to build and optimize our large-scale AI workloads
• Analyze, benchmark, and optimize large-scale workloads on next-generation training superclusters
• Define use cases and develop methodology and benchmarks to evaluate different approaches
• Set direction and goals for the team related to project impact, AI system design, infrastructure, and developer efficiency
• Lead large and complex technical efforts across many engineers and teams
• Influence and impact next-generation of model and hardware architecture choices through deep and thorough data-driven analyses
• Helps onboard new team members, provides mentorship, and enables successful ramp up on the team's code base
• Mentor other engineers, research scientists and improve the quality of engineering work in the broader team

• Bachelor's degree in computer science or a related STEM field
• Specialized experience in one or more of the following machine learning/deep learning domains: hardware accelerator architectures, machine learning compilers or ML systems, AI infrastructure, high-performance computing, performance optimizations or ML frameworks such as PyTorch
• Proven C/C++ and Python programming skills in developing AI Systems infrastructure or AI algorithms
• Experience with debugging in C++, Python and/or PyTorch
• Track record of mentoring and growing other engineers
• Must obtain work authorization in the country of employment at the time of hire, and maintain ongoing work authorization during employment

• Master's degree/PhD in computer science or related STEM field and strong experience in AI framework development or accelerating deep learning models on hardware architectures
• Experience with training of large-scale AI models
• Experience with distributed AI systems and communication protocols such as MPI or collective libraries such as NCCL
• Experience or knowledge in one or more of LLM/LDM, ranking and recommender models or collective communication libraries