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.
The CHAI Research Fellowship
Start Date: Flexible
Compensation: Negotiated
Apply: Via this form
CHAI Research Fellows will work with CHAI faculty members Stuart Russell, Pieter Abbeel, and Anca Dragan and will also have the opportunity to collaborate with CHAI affiliate faculty at Berkeley, other faculty members in Berkeley AI Research (where CHAI is located), and many other institutions. The Fellowship is aimed at training highly qualified postdoctoral researchers to carry out research to advance beneficial AI.
Duties of position
Given the broad and open-ended mandate of the Center to develop provably beneficial AI systems, the post holder will have considerable freedom to pursue novel research projects in areas such as reasoning, decision making, learning, multi-agent systems, and philosophical foundations using the tools of probability theory, game theory, control theory, etc. Topics of particular current interest include
- Theoretical foundations of intelligent agent architecture
- Expressive formal languages for probability models, including probabilistic programming
- Decision making over long time scales
- Cumulative lifelong learning
- Human-in-the-loop planning and reinforcement learning
- Human-robot interaction
- The structure and properties of human preferences
- Foundations for acting on behalf of multiple humans
- Robust cooperation in heterogeneous multiagent systems
- Mechanism design for human-machine systems
Depending on their interests, Fellows may also lead research collaborations with other CHAI researchers and Berkeley PhD students, advise more junior student researchers, and teach courses relevant to their research. Candidates for the Fellowship are assessed based on their academic and research achievements, as well as on personal merit and leadership qualities.
Qualifications
Candidates 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. Candidates need not have done previous work on the AI control problem.
Salary & Visa Sponsorship
The CHAI Research Fellowships provide funding for two years, renewable up to three years given satisfactory performance. Salary will be commensurate with qualifications and experience and based on UC Berkeley salary scales. The position is eligible for visa sponsorship. CHAI will also contribute to visa costs for Fellows and their dependents (partner and children).
Application
Apply via this form. Supporting documents include a resume, a one-page statement of interest describing in general terms the kind of research you would like to undertake at CHAI, and the names and email addresses of two academic referees.
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 fill out this form.
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 CHAI Internship application is currently closed and will reopen Fall 2024. Please subscribe to our newsletter for updates regarding the application.
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:
- Image Hijacks: Adversarial Images can Control Generative Models at Runtime by Luke Bailey*, Euan Ong*, Stuart Russell, and Scott Emmons, 2023
- 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, Workshop on Learning with Rich Experience, 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
- Learning What To Do by Simulating the Past by David Lindner, Rohin Shah, Pieter Abbeel, Anca Dragan. In 9th International Conference on Learning Representations (ICLR 2021).
- On the Utility of Learning about Humans for Human-AI Coordination by Micah Carroll, Rohin Shah, Mark Ho, Tom Griffiths, Sanjit Seshia, Pieter Abbeel, Anca Dragan. In Neural Information Processing Systems (NeurIPS 2019).
- An Empirical Investigation of Representation Learning for Imitation. Xin Chen*, Sam Toyer*, Cody Wild*, Scott Emmons, Ian Fischer, Kuang-Huei Lee, Neel Alex, Steven Wang, Ping Luo, Stuart Russell, Pieter Abbeel, Rohin Shah. In NeurIPS 2021 Datasets and Benchmarks Track.
UC Berkeley Values and Principles
We are looking for equity-minded applicants who represent the full diversity of California and who demonstrate a sensitivity to and understanding of the diverse academic, socioeconomic, cultural, disability, gender identity, sexual orientation, and ethnic backgrounds present in our community. When you join the team at Berkeley, you can expect to be part of an inclusive, innovative and equity-focused community that approaches higher education as a matter of social justice that requires broad collaboration among faculty, staff, students and community partners. In deciding whether to apply for a position at Berkeley, you are strongly encouraged to consider whether your values align with our Guiding Values and Principles, our Principles of Community, and our Strategic Plan.
Equal Employment Opportunity
The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status. For more information about your rights as an applicant see here. For the complete University of California nondiscrimination and affirmative action policy see here.