Full-Time

Senior Hardware Engineer

Physical Design

Confirmed live in the last 24 hours

DeepMind

DeepMind

1,001-5,000 employees

Develops artificial general intelligence systems

AI & Machine Learning
Biotechnology

Compensation Overview

$142k - $219kAnnually

+ Bonus + Equity + Benefits

Expert

Mountain View, CA, USA

Category
Hardware Engineering
Hardware Validation & Testing
Required Skills
Bash
Python
Machine Learning

You match the following DeepMind's candidate preferences

Employers are more likely to interview you if you match these preferences:

Degree
Experience
Requirements
  • At least 10 years experience in ASIC physical design flows and methodologies in advanced nodes.
  • Experience from PD for high performance compute IPs (e.g., GPUs, DSPs, or machine learning accelerators).
  • Successful track record of delivering tape-outs to production.
  • Capability and strong willingness to work in PD in a research environment.
  • Hands on experience and a solid understanding of ASIC physical design, physical design flows and methodologies including synthesis, place and route, STA, Formal Verification, CDC and Power Analysis using tools such as Design Compiler, FC, Innovus, PrimeTime, PrimeTime-PX, Calibre, ICV, Conformal, RedHawk, Spyglass and PowerArtist.
  • Strong scripting skills in Python, TCL, BASH.
Responsibilities
  • Work in a fast and interdisciplinary team bringing together experts from Machine Learning, Hardware, Programming Languages and Systems.
  • Work in close collaboration with HW architects and design engineers rapidly iterating experimental designs, giving rapid yet reliable feedback on the performance, power and area of different design options.
  • Drive architectural feasibility studies, explore RTL/design tradeoffs for physical design closure.
  • Perform block level physical implementation steps including synthesis, floorplanning, place and route, power/clock distribution, congestion analysis, STA, timing closure, EM-IR, PV, CDC analysis, LEC etc.
  • Provide actionable feedback to silicon design engineers and architects for design improvements.
  • Participate in establishing physical design methodologies, flow automation, chip floorplan, power/clock distribution, chip assembly and P&R, timing closure.
  • Develop physical design methodologies and automation scripts for various implementation steps.
  • Perform technical evaluations of vendors, process nodes, IP and chip design tools.
Desired Qualifications
  • Experience from multiple foundries.
  • Experience from working with multiple EDA vendors.
  • Experience with leading one or more aspects of physical design.
  • Experience in IP integration (memories, IO’s and Analog IP).
  • Experience solving physical design challenges across various technologies such as embedded processors, ML-Accelerators, networking fabrics, etc.
  • Experience in extraction of design parameters, QOR metrics, and analyzing trends.
  • Working knowledge of semiconductor device physics and transistor characteristics.
  • Experience or understanding of fullchip floorplanning, C4 & bus planning.
  • Understanding of custom macro blocks such as RAM/ROM, SerDes, PCIe, memory controllers.
  • Working knowledge of Verilog/System Verilog.

This company leads in the field of artificial general intelligence (AGI), with notable applications across healthcare, energy management, and biotechnology. Their work in early diagnostic tools for eye diseases, optimizing energy usage in major data centers, and groundbreaking contributions to protein structure prediction underlines their commitment to harnessing AI for diverse practical applications. The company's dedication to pushing the boundaries of AI technology not only propels the industry forward but also creates a dynamic and impactful working environment for its employees.

Company Size

1,001-5,000

Company Stage

Acquired

Total Funding

$4.9M

Headquarters

London, United Kingdom

Founded

2010

Simplify Jobs

Simplify's Take

What believers are saying

  • AI-driven drug discovery is set to grow significantly in 2024.
  • AlphaCode 2 showcases AI's potential in competitive programming.
  • DeepMind's AI tools are transforming music creation and meteorology.

What critics are saying

  • Emerging AI models may challenge DeepMind's current strategies.
  • Backlash against AI models like Gemini poses reputational risks.
  • Labeling AI-generated content could increase operational complexity for DeepMind.

What makes DeepMind unique

  • DeepMind combines AI, ML, and neuroscience for general-purpose learning algorithms.
  • DeepMind's AlphaFold model advances protein folding research significantly.
  • GraphCast by DeepMind offers rapid, accurate ten-day weather forecasts.

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Benefits

Performance Bonus

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