Google DeepMind AI Tools

Benefits:

• enhanced maternity, paternity, adoption, and shared parental leave

• private medical and dental insurance for yourself and any dependents

• flexible working options

• healthy food

• an on-site gym

Education Requirements:

• PhD in a technical field or equivalent practical experience

Experience Requirements:

• Knowledge of the latest in large machine learning research

• Experience working with simulators and real-world robots

• Expertise in using large datasets with deep neural networks to make real robots useful

• A real passion for AI impacting real world robots!

Responsibilities:

• Design, implement and evaluate models and software prototypes of robotic agents.

• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.

• Suggest and engage in team collaborations to meet ambitious research goals.

• Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate

Benefits:

• enhanced maternity, paternity, adoption, and shared parental leave

• private medical and dental insurance for yourself and any dependents

• flexible working options

• healthy food

• an on-site gym

Education Requirements:

• PhD/MS in Computer Science or Robotics; or equivalent practical experience

Experience Requirements:

• Experience working with real-world robots, preferably Humanoid platforms

• Strong exposure to Machine Learning/AI systems

• Experience on solving practical Safety challenges, performing Risk Assessments

• Working with different stakeholders to define and implement safety guidelines to ensure the safety of humans and robots in mixed environments, and also ensure that research velocity is not compromised

• Understanding of ISO/RIA Safety Standards

Responsibilities:

• Performing Hazard and Risk Analysis for emerging Robotics Platforms

• Provide expertise and guidance on Implementing state-of-the-art safety solutions on our robots and workcells, to enable safe data collection and model evaluation

• Working closely with Google DeepMind Robotics Researchers to accelerate development of new capabilities while ensuring safety.

• Coordinating with Environment, Health and Safety (EHS) teams to ensure consistency across GDM Robotics Labs.

• Preparing Safety training materials

Benefits:

• enhanced maternity, paternity, adoption, and shared parental leave

• private medical and dental insurance for yourself and any dependents

• flexible working options

• healthy food

• an on-site gym

Education Requirements:

• At least 10 years experience in ASIC physical design flows and methodologies in advanced nodes

Experience Requirements:

• 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.