Start date: ASAP
Status: Full-time employee
Compensation: $125,000 annually
Location: The San Francisco Bay area
Reports to: CHAI Collaboration Manager
Application: at the bottom of this page
BERI is seeking to hire a machine learning engineer to collaborate with the Center for Human Compatible AI (CHAI) under UC Berkeley professor Stuart Russell. Pending final evaluation from CHAI, successful candidate(s) will be offered a 1-2 year visiting researcher scholar position at UC Berkeley to work with Professor Stuart Russell’s research group (CHAI’s Listing), alongside Research Scientist Andrew Critch, and with opportunities to collaborate with CHAI’s co-Principal Investigators at Berkeley (Pieter Abbeel, Anca Dragan, Tania Lombrozo), Cornell (Bart Selman, Joe Halpern), Michigan (Michael Wellman, Satinder Singh) and Princeton (Tom Griffiths), as well as with groups at Cambridge, Oxford, and Imperial College through the Leverhulme Centre for the Future of Intelligence. As global demand for AI safety research increases, we expect the experience gained from this work will be valued internationally.
If any of these projects interests you, we encourage you to apply:
Taking responsibility for the existence of an easily usable shared codebase for CHAI researchers. This would include open-sourcing inverse reinforcement learning (IRL) packages currently under development at CHAI.
Developing algorithmic implementations of Negotiable Reinforcement Learning.
Writing or adapting existing code to reproduce natural language processing (NLP) results from Stanford researchers, e.g.
- Naturalizing a Programming Language via Interactive Learning (Wang, Ginn, Liang, Manning; 2017)
- From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood (Guu, Pasupat, Liu, Liang; 2017)
- Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings (He, Balakrishnan, Eric, Liang; 2017)
Measuring the combined efficacy of goal-inference algorithms (such as IRL) with planning algorithms (such as Point-Based Value Iteration) in mechanism design applications (e.g. an algorithm playing a game on behalf of two players who have different beliefs about the game)
To read more about why we are interested in hiring machine learning engineers, see this blog post.
We are especially interested in applicants who can take initiative in finding ways to help out with research at CHAI. This role involves figuring out what would be helpful for the research team and then doing it.
- Solid software engineering skills across multiple languages, ideally including Python and C/C++
- Experience with machine learning software packages (e.g. TensorFlow, PyTorch)
- Practical experience building machine learning or AI systems. This could be demonstrated by professional work experience, previous research papers or open-source contributions
- Strong analytical and problem-solving skills
- Excellent technical communication skills, the ability to elaborate complex technical concepts and collaborate effectively with fellow engineers and researchers
- Familiar with core CS concepts such as common data structures and algorithms
- Comfortable conducting design and code reviews
- Prior research or research engineering experience
- Written work on ML or AI, including technical blog posts or publications in major conferences or journals
- Distributed systems and basic DevOps experience to manage in-house and cloud servers for experiments (e.g. Terraform/Chef, Kubernetes/Mesos, Docker)
- BS/BA, MS, or ideally PhD in computer science, data mining, machine learning, information retrieval, recommendation systems, natural language processing, statistics, math, engineering, operations research, or other quantitative discipline
- Time-off (paid vacation, holidays, sick leave, bereavement leave, & parental leave)
- Generous professional development policy
- Health insurance
- Semi-flexible work schedule including hours, location, and unpaid vacation policies
BERI is proud to be an Equal Employment Opportunity employer. Our mission to improve human civilization’s long-term prospects for survival and flourishing is in service of all of humanity, and is incompatible with unfair discrimination practices that would pit factions of humanity against one another. We do not discriminate against qualified employees or applicants based upon race, religion, color, national origin, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, sexual preference, marital status, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, genetic information, or any other characteristic protected by federal or state law or local ordinance. We also consider qualified applicants with criminal histories, consistent with applicable federal, state and local law.