Senior Systems ML Research Scientist — HW/SW Co-Design

Meta; Menlo Park (CA); On-site; USD 220,000 - 300,000; Full time

Job summary

Meta is seeking a Research Scientist for the AI & Systems Co-Design team. The candidate will drive next-generation AI accelerator architecture through hardware/software co-design, shaping MTIA's hardware roadmap from workload characterization to pre-silicon validation.

You will collaborate with silicon design, ML infrastructure, and product teams to ensure hardware is purpose-fit for AI workloads of tomorrow, including LLMs, recommendation systems, and generative AI.

Qualifications

• Bachelor's degree in CS, CE or related field; 7+ years industry experience.
• PhD preferred; experience with AI workloads and hardware cross-design.
• Experience with silicon tapeout and pre-silicon validation.
• Experience with ML workloads and hardware impact on scale.

Responsibilities

• Shape MTIA architecture by translating ML workload insights into hardware design decisions.
• Lead pre-silicon decision-making with data-driven analysis of micro-architectures.
• Build evaluation infrastructure for rapid design trade-off exploration.
• Coordinate across silicon design, ML infra, and product teams at scale.
• Define benchmarks and evaluation criteria for hardware architecture options.
• Bridge ML and hardware to drive architectural innovations.
• Mentor and raise technical rigor across teams.

Skills

Hardware/software co-design

AI accelerator architecture

Systems for ML

Performance modeling

PPA trade-offs

Silicon tapeout

ML frameworks PyTorch

Cross-team leadership

Mentoring engineers

Education

PhD in Computer Science/Engineering

Bachelor’s degree in CS/CE/EE

Tools

PyTorch

Job description

Meta is seeking a Research Scientist for the AI & Systems Co-Design team. The candidate will drive next-generation AI accelerator architecture through hardware/software co-design, shaping MTIA's hardware roadmap from workload characterization to pre-silicon validation.

You will collaborate with silicon design, ML infrastructure, and product teams to ensure hardware is purpose-fit for AI workloads of tomorrow, including LLMs, recommendation systems, and generative AI.