Robot Learning Intern (PhD) - Analog Devices - Boston, Massachusetts, United States

Are you a problem solver looking for a hands-on internship position with a market-leading company that will help develop your career and reward you intellectually and professionally?

About Analog Devices

Analog Devices, Inc. (NASDAQ: ) is a global semiconductor leader that bridges the physical and digital worlds to enable breakthroughs at the Intelligent Edge. ADI combines analog, digital, and software technologies into solutions that help drive advancements in digitized factories, mobility, and digital healthcare, combat climate change, and reliably connect humans and the world. With revenue of more than $9 billion in FY24 and approximately 24,000 people globally, ADI ensures today's innovators stay Ahead of What's Possible™. Learn more at and on and .

At ADI, you will learn from the brightest minds who are here to help you grow and succeed. During your internship, you will make an impact through work on meaningful projects alongside a team of experts. Collaborating with colleagues in an environment of respect and responsibility, you will create connections that will become a part of your professional network.

ADI’s culture values aligned goals, work-life balance, continuous and life-long learning opportunities, and shared rewards. The internship program features various lunch-and-learn topics and social events with other interns and full-time employees.

At ADI, our goal is to develop our interns so they are the first to be considered for full-time roles.

Apply now for the opportunity to grow your career and help innovate ahead of what’s possible.

The Dexterous AI Group (DAG) is looking for Robot Learning Engineer Intern to develop core AI technologies for Analog Devices’ future AI robotics with generality and dexterity, beyond the reach of traditional algorithms and system innovations.

Responsibilities include:

• Contribute the development of advanced learning algorithms for general and dexterous robot using the state-of-the-art techniques.

• Create sandbox simulations, deployable implementations, and evaluation frameworks for algorithm performance and robustness.

• Contribute to data requirements, data collection setup and procedure, and data curation.

• Stay abreast of the latest developments in machine learning and robotics from reputable groups.

Qualifications:

• Currently pursuing Ph.D. degree in relevant area

• Strong background in machine learning and robotics, and experience in:

  • Planning and control algorithms (e.g., A*, MPC).

  • Optimization techniques (e.g., linear/nonlinear optimization).

  • Training deep learning models with PyTorch.

  • Reinforcement Learning and Imitation Learning.

• Preferred: familiarity with

  • foundation models and VLA models.

  • robotics toolkits (e.g., ROS/ROS2, Gazebo, Isaac)

  • robotic systems and mechanical design

For positions requiring access to technical data, Analog Devices, Inc. may have to obtain export licensing approval from the U.S. Department of Commerce - Bureau of Industry and Security and/or the U.S. Department of State - Directorate of Defense Trade Controls. As such, applicants for this position – except US Citizens, US Permanent Residents, and protected individuals as defined by 8 U.S.C. 1324b(a)(3) – may have to go through an export licensing review process.

Analog Devices is an equal opportunity employer. We foster a culture where everyone has an opportunity to succeed regardless of their race, color, religion, age, ancestry, national origin, social or ethnic origin, sex, sexual orientation, gender, gender identity, gender expression, marital status, pregnancy, parental status, disability, medical condition, genetic information, military or veteran status, union membership, and political affiliation, or any other legally protected group.

EEO is the Law: Notice of Applicant Rights Under the Law .

Job Req Type: Internship/Cooperative

Required Travel: No

The expected wage range for a new hire into this position is $22 to $41.

Actual wage offered may vary depending on work location , experience, education, training, external market data, internal pay equity, or other bona fide factors.