Research Hardware Engineer, Tactile Sensing, DeepMind

Minimum qualifications:

• Bachelor's degree in Materials Science, Robotics, Electrical Engineering, or a relevant technical field, or equivalent practical experience.
• 4 years of experience working on tactile sensing or flexible electronics.

Preferred qualifications:

• PhD in Materials Science, Robotics, Electrical Engineering, or a relevant technical field.
• 8 years of experience with published contributions to the field of tactile sensing or flexible electronics.

About the job:

As a Tactile Sensing Researcher, you will not only select off-the-shelf sensors; you will invent, design, and validate novel transduction methods. You are a first-principles researcher that understands how material properties can be utilized to create novel sensing solutions. You will bridge the gap between material science and sensing, creating thin, conformal sensing arrays adaptable to a range of end effectors in support of our dexterous manipulation research across many robot embodiments.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

Responsibilities:

• Own the exploration and design of novel tactile sensing modalities tailored for a versatile suite of custom robotic manipulators.
• Utilize expert simulation tools (e.g., COMSOL, Ansys) to model the complex interactions between materials and sensing elements.
• Define technical fabrics and elastomeric substrates with specific mechanical properties and implement manufacturing techniques to prototype novel sensor arrays.
• Partner with AI researchers to align sensor hardware with manipulation algorithms, ensuring tactile data is effectively utilized for closed-loop control across various form factors.
• Establish testing protocols to evaluate sensor performance in simulation-to-reality environments across multiple manipulation platforms.