About this role
Meta’s Reality Labs is seeking a Digital Design, SOC Integration Lead to serve as a technical lead for Machine Learning IP integration. In this role, you will drive the integration of Machine Learning IP blocks into complex system-on-chip designs, bringing together expertise in design, integration, and physical design. You will be responsible for project planning, tracking, and execution while working closely with cross-functional teams, SoC teams, and IP vendors to deliver next-generation ML accelerators that power Meta's AR and VR devices.
Responsibilities
Serve as the technical lead for ML IP integration, driving end-to-end integration from RTL to physical design handoff
Own project planning, tracking, and execution for ML IP integration efforts
Lead design and integration activities, including microarchitecture, RTL design, lint, CDC, synthesis, and physical design collaboration
Partner with cross-functional teams, SoC teams, and external IP vendors to ensure a seamless integration
Define and drive integration methodologies, flows, and best practices across the silicon team
Drive root cause analysis and resolution of issues across design, synthesis, and physical implementation stages
Mentor and guide engineers on ML IP integration and design best practices
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
12+ years of experience in digital design and IP/SOC integration
Experience serving as a technical lead for complex IP or SOC integration projects
Experience with RTL design, integration, lint, CDC, and synthesis flows
Experience with physical design concepts and collaboration with P.D. teams
Experience with project planning, tracking, and execution for silicon programs
Experience working with cross-functional teams and external IP vendors
Experience with methods for partitioning a solution across hardware and software, evaluating trade-offs such as speed, performance, power, and area
Experience with scripting languages such as Python, Perl, or Tcl
Bachelor's degree in Electrical Engineering, Computer Engineering, or relevant field Experience with low-power design techniques and UPF flows
Master/PhD degree in EE/CS or equivalent areas
Experience with physical design flows, floorplanning, and timing closure
Experience with machine learning accelerator hardware design and integration