[We are not currently hiring for this role.]
BERI is seeking to hire a full-time Machine Learning Team Lead to grow and manage a team of machine learning engineers to work in collaboration with researchers at UC Berkeley, Stanford, and other ML research groups in the Bay Area.
Qualifications needed: A demonstrated track record of
- research and publication in machine learning;
- managing a team of at least 3 machine learning engineers with impressive collective output;
- seeking out and initiating novel collaborations; and
- a serious interest in existential risk reduction.
Status: Full-time employee, salaried
Start date: As soon as a promising candidate is identified
Compensation: $100k-$150k, negotiated
Typical hours: 40 per week, primarily during standard business hours.
Work Location: Berkeley/Oakland
Reports to: Executive Director
Application: at the bottom of this page
The purpose of BERI’s engineering team will be threefold:
- To serve as a rich source of collaborators for AI researchers at UC Berkeley, Stanford, and elsewhere in the Bay Area who are getting interested in x-risk, as a career incentive gradient toward prioritizing work relevant to x-risk;
- To prepare x-risk-motivated AI PhD students at UC Berkeley and Stanford to excel in a professional environment that involves collaboration with and/or managing engineers; and
- To directly increase the x-risk-relevant research output of BERI’s collaborators.
Purposes 1 and 2 may be viewed as improving the career pipeline of AI researchers interested in existential risk (see this blog post outlining that vision), and seems highly achievable based on experience with our first ML engineer contractor. Purpose 3 is somewhat more ambitious, because of the difficulty of ensuring that any particular research project will turn out to be effective for existential risk reduction, but also seems achievable based on available research directions.
We have wide error bars around how the ML Team Lead would spend their time, and details would be highly contingent and adaptive to the availability of academic collaborators.
During periods of high demand, the ML Team Lead would focus on
- Inquiring at length as to the x-risk-relevance of various project ideas from potential collaborators, to assess those collaborators for
- their interest in existential risk reduction,
- their ability to prioritize relevant research based on that interest, and
- the relative value of their proposed research directions.
- Recruiting more machine learning engineers as needed to fulfill demand for high quality research directions
During periods of low demand, the ML Team Lead would focus on
- Ensuring our engineering staff have sufficiently valuable projects to work on as to warrant the opportunity cost of their time and funding, by
- reaching out to potential collaborators at UC Berkeley and Stanford;
- meeting with existing collaborators to ensure BERI’s resources are being used to their full potential;
- drafting project proposals that the CTO could manage directly, for circulation and discussion among our most x-risk-motivated collaborators to get feedback on the potential value of those projects.
Does this sound like it could be you?