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Silicon Physical Design Engineer

Meta
Posted 4 months ago, valid for a month
Location

Burlingame, San Mateo 94011, CA

Salary

$178,000 - $250,000 per year

Contract type

Full Time

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Sonic Summary

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  • Meta is seeking an experienced engineer to join the Reality Labs Silicon team, focusing on advanced AI-enabled wearables.
  • The role requires a minimum of 8 years of experience in ASIC Physical Design and a Bachelor's degree in a relevant technical field.
  • Key responsibilities include developing physical design implementations for low-power ML hardware and collaborating with ML architects to optimize design methodologies.
  • Candidates should have experience with EDA tools and programming languages such as Python, TCL, and Perl.
  • The salary for this position ranges from $178,000 to $250,000 per year, plus bonuses, equity, and benefits.
Meta’s mission is to give people the power to build community and bring the world closer together. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities. We are just getting started. Reality Labs focuses on delivering Meta's vision through creating advanced AI enabled wearables. The compute performance and power efficiency requirements of wearables require custom silicon. Reality Labs Silicon team is driving the state-of-the-art forward with breakthrough work in AI, augmented reality, computer vision, machine learning, graphics, displays, and sensors Our chips will enable wearable devices where our real and virtual world will mix and match throughout the day. We believe the only way to achieve our goals is to look at the entire stack, from transistors, through architecture, firmware, and algorithms. In this position, you will work with Machine Learning (ML) front-end and back-end hardware designers to drive the Physical design implementation of ML compute blocks in advanced technology nodes and develop custom methodologies to optimize the PPA of the design.

Responsibilities

  • Develop and own physical design implementation of multi-hierarchy low-power ML Hardware design including physical-aware logic synthesis, floorplan, place and route, static timing analysis, IR Drop, EM, and physical verification in advanced technology nodes
  • Resolve design and flow issues related to physical design, identify potential solutions, and drive execution
  • Collaborate with ML architects and designers to understand the ML workloads and develop custom physical design methodologies and recipes to optimize the PPA of ML compute datapath design blocks
  • Work across disciplines, brainstorm big ideas, work in new technology areas, juggle/coordinate multiple initiatives, drive a concept into a prototype and ultimately guide the transition into a high-volume consumer product
  • Travel both domestically and internationally


Minimum Qualifications

  • Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
  • 8+ years of experience in ASIC Physical Design
  • Understanding of RTL2GDSII flow and design tapeouts in 3nm or below process technologies
  • Experience with low power implementation, power gating, multiple voltage rails, UPF knowledge
  • Experience working with EDA tools like Fusion Compiler, ICC2/Innovus, Primetime, RedHawk
  • Experience with Python, TCL, Perl programming


Preferred Qualifications

  • Experience running physical-aware logic synthesis and achieving optimal synthesis QoR on low power designs
  • Master/PhD degree in EE/CS or equivalent areas
  • Experience with custom or regular clock tree synthesis implementation at block level or top level, and clock power reduction techniques
  • Knowledge of static timing analysis and concepts, defining timing constraints and exceptions, corners/voltage definitions
  • Experience in Block-level and Full-chip floor-planning and power grid planning


$178,000/year to $250,000/year + bonus + equity + benefits



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