Work With Us
We seek to fill the following positions:
If none of these positions are the right fit for you but you would still like to express an interest in working with us, please fill out this form.
General enquiries about jobs at CHAI can be sent to chai-info@berkeley.edu.
Postdoc Specializing in AI Safety and Control
Start Date: Flexible
Compensation: Negotiated
Apply: Via this form
If you are a graduating PhD student, taking a postdoc at CHAI before moving into industry or a faculty position can help you to develop an expertise in the field of AI safety and control, positioning you to become at leader in this developing field.
Successful candidates will work with the CHAI Director, Stuart Russell, or with one of the Berkeley co-Principal Investigators, Pieter Abbeel, Anca Dragan, and Tom Griffiths. There will also be opportunities to collaborate with CHAI investigators at Cornell
(Bart Selman, Joe Halpern) and Michigan (Michael Wellman, Satinder Singh), as well as with groups at Cambridge, Oxford, and Imperial College through the Leverhulme Centre for the Future of Intelligence.
Developing provably beneficial AI systems will require a significant reorientation of the general thrust of AI research, which up to now has been largely concerned with designing systems that optimize exogenously specified objectives. Given the broad and open-ended mandate of the Center, the post holder will have considerable freedom to pursue novel research projects within
CHAIâs areas of interest, either individually or working with PhD students and undergraduate researchers.
Early examples of CHAIâs work include a recent paper on cooperative inverse
reinforcement learning (to appear in NIPS 16).
Related work includes some results from FHIâs
collaboration with Google DeepMind and the descriptions of
research problems by Google Brain and by the
Machine Intelligence Research Institute
Candidates need not have done previous work on the AI control problem but must have (or be about to obtain) a PhD in a relevant technical discipline (computer science, statistics, mathematics, or theoretical economics) and a record of high-quality published research. A solid understanding of current methods in AI and statistical learning would be an advantage.
Research Collaborators
Our core research spans computer science, mathematics, control theory, robotics, statistics, formal logic, economics (including game theory), cognitive psychology, or neuroscience. We may also be interested in moral philosophy, sociology, political science, law, and other fields dealing with formal and semiformal theories of human value systems.
If you are interested in collaborating on research that aligns with our mission, please email chai-info@berkeley.edu. Please include information about yourself and your organization, as well as details of how you would like to collaborate, and why CHAI would be a good fit.
If you are currently at UC Berkeley, you are welcome to attend our weekly research seminars. Contact chai-info@berkeley.edu for details.
NOTE: Undergraduates interested in graduate study at Berkeley in this area should apply directly to the appropriate UC Berkeley graduate program. You may mention your interest in CHAI on your application, but we are unable to review or give feedback on your application. We will be happy to discuss research and/or supervision once you have been admitted.
CHAI Internships
The internship application will open in Fall 2021. If you would like to be notified when the internship application reopens, please subscribe to our newsletter.
CHAI interns work on a research project supervised, and typically proposed, by a mentor. Most projects are primarily executed by the intern, while the mentor provides advice and guidance; these are typically published as first-author workshop papers. On occasion, a mentor may propose a more team-based project that they or other interns are also executing on; these are typically published as conference papers. In addition, interns are encouraged to participate in other activities in CHAI, such as our weekly seminars, annual workshop and team socials.
To view some of our former interns’ profiles, click here. Former interns have developed essential skills and knowledge, preparing them for graduate school and industry opportunities. Take a look at what they had to say:
Highlighted papers:
- Preferences Implicit in the State of the World by Rohin Shah, Dmitrii Krasheninnikov, Jordan Alexander, Pieter Abbeel, and Anca Dragan, ICLR 2019
- Combining Reward Information from Multiple Sources by Dmitrii Krasheninnikov, Rohin Shah, and Herke van Hoof, NeurIPS 2019
- Active Inverse Reward Design by Sören Mindermann, Rohin Shah, Adam Gleave, and Dylan Hadfield-Menell, Workshop on Goal Specifications for Reinforcement Learning, FAIM 2019
- An Extensible Interactive Interface for Agent Design by Matthew Rahtz, James Fang, Anca Dragan, and Dylan Hadfield-Menell, Workshop on Human in the Loop Learning, ICML 2019
- Exploring Hierarchy-Aware Inverse Reinforcement Learning by Chris Cundy and Daniel Filan, Workshop on Goal Specifications for Reinforcement Learning, ICML 2018