Research Scientist, AI & Systems Co-Design (PhD)

Meta; New York (NY); On-site; USD 170,000 - 230,000; Full time

Job summary

Meta seeks a PhD-level researcher to advance hardware-aware AI model enablement and performance modeling for AI accelerators. The role emphasizes end-to-end architecture, on-device mapping, and working with CNNs, Transformers, LLMs, and diffusion models.

The candidate will analyze system-level performance, collaborate with cross-functional teams, and publish results at top venues while contributing to production-grade AI workloads and optimization strategies.

Qualifications

• Proven research experience in hardware-aware model enablement or AI accelerator performance modeling.
• Hands-on end-to-end AI hardware architecture and on-device mapping algorithm development.
• Background with CNNs, Transformers, LLMs, or diffusion models.
• Experience in system-level performance analysis, profiling, benchmarking AI workloads.
• Proficiency in Python and at least one major AI framework.
• Track record of publishing research and communicating results cross-functionally.
• Experience deploying AI agents or pretrained techniques for efficiency.
• Experience or knowledge of large-scale training/inference.
• Familiarity with low-level programming for specialized hardware or HDL.
• Experience or knowledge of distributed ML systems.
• Knowledge of Generative AI models or Ranking/Recommendation models.

Skills

Python

AI frameworks

Performance modeling

System-level analysis

Distributed ML

Research publication

Communication

CUDA

Triton

LLMs/Transformers

Education

PhD in Computer Science or Electrical Engineering

Master's +3 years industry

Tools

CUDA

HDL

Triton

DeepLearning frameworks

Job description

Our teams’ mission is to explore, develop and help productionize high performance software & hardware technologies for AI at datacenter scale. 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, new model architectures and reduces cost of ownership for all key AI services at Meta: 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 product teams, 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.

In addition to the real-world impact on billions of users of the Meta products, our team members have won Best Paper Awards at prestigious conferences such as ISCA, ASPLOS, SOSP, and OSDI, with multiple papers selected for IEEE Micro Top Picks. We regularly publish in ICML, NeurIPS, SC, HPCA, NSDI, VLDB, MLSys, and more. Overall, our work largely corresponds to the research communities of systems in general and especially systems for ML (MLSys, SOSP, OSDI, SIGCOMM, NSDI), hardware architecture (ISCA, ASPLOS), ML (NeurIPS, ICML, ICLR) and supercomputing (SC,ICS).

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta PhD in Computer Science, Electrical Engineering, Applied Mathematics, or a related technical field, OR a Master's degree with 3+ years of relevant industry experience.

• Proven research experience in one or more of the following areas: hardware‑aware model enablement, performance modeling of AI systems or prevailing accelerators/silicon architectures
• Hands‑on proficiency with end‑to‑end AI hardware architecture or on‑device mapping algorithm development, encompassing logic, architecture, and optimizations for performance, power, and area (Power, Performance, and Area) (PPA)
• Theoretical background and practical experience with AI models (e.g., CNNs, Transformers, LLMs, Diffusion models)
• Experience in system‑level performance analysis, profiling, and benchmarking of AI workloads
• In‑depth experience of Python and experience with at least one major AI framework
• Track record of publishing research papers at peer‑reviewed conferences or journals, and experience communicating technical results to cross‑functional stakeholders
• Experience with deploying AI agents/prevalining techniques for increased efficiency
• Experience or knowledge of training/inference of large‑scale deep learning models
• Familiarity with low‑level programming for specialized hardware (e.g., CUDA, HIP, Triton) or hardware description languages (HDL)
• Experience or knowledge of distributed ML systems and algorithm development
• Experience or knowledge of either Generative AI models such as LLMs/LDMs or Ranking & Recommendation models such as DLRM or equivalent