{"schemaVersion":"jobsearcher.job.v1","id":"d64924da1bb5c5f1584301a0","url":"https://jobsearcher.com/jobs/d64924da1bb5c5f1584301a0","canonicalUrl":"https://jobsearcher.com/jobs/d64924da1bb5c5f1584301a0","title":"Research Scientist","description":"About Sequent\nSequent does research on how to align artificial superintelligence (ASI). ASI may be developed in the next few years, but it is unclear whether alignment is on track to be ready in the same timeframe. We aim at higher a priori confidence in aligned outcomes by pursuing a portfolio of theory and empirics bets, any one of which — if it succeeds — would meaningfully advance the field. We invest heavily in research automation to accelerate progress, and we believe that stronger alignment theory unlocks higher automation: more principled approaches give us better filters for which directions of automated research are promising.\n\nSequent was founded in May 2026 by researchers from UK AISI's Alignment Team, who ran the £30m Alignment Project, and Timaeus, who pioneered applying singular learning theory to alignment.\n\nFor more information, see our announcement.\n\nResearch at Sequent\nWe work across a portfolio of research areas, currently including:\n\nScalable oversight : empirical work on protocols (debate, recursive reward modeling, prover-verifier games) that allow weaker overseers to supervise stronger systems, paired with complexity-theoretic work on equilibria and reachability.\n\nComplexity theory : theoretical work on modeling the interaction of superintelligent agents with lower complexity training environments, including applications to scalable oversight, heuristic arguments, and other agendas.\n\nLearning theory : singular learning theory and its applications, deep learning theory, computational mechanics, etc.\n\nPersonas : theory and empirics of low-dimensional structure within model behavior across training and token dimensions.\n\nThe full set of bets has not yet been finalized but, in the future, may include further agendas like:\n\nHeuristic arguments : mechanistic understanding of what models know, low-probability estimation.\n\nGame theory : mechanism design, agent foundations, open-source game theory.\n\nA cross-cutting focus is research automation : building infrastructure and tooling to scale all of the above by leveraging AI research assistants at every level of the stack, across both theoretical and empirical work.\n\nAbout the Role\nAs a Research Scientist at Sequent, you'll work as part of a research program led by a senior researcher (which could be you!). We're hiring across our research programs, and the team you'd join depends on your strengths and interests — in your application, indicate which program(s) you'd be most interested in.\n\nOur goal of reaching higher confidence in alignment will require significant new contributions in both methods and scientific understanding. The work you do day-to-day will be part of a collaborative research program; all programs involve deep technical research, with strong support for cross-program collaboration. We welcome both empirical and theoretical profiles.\n\nResponsibilities\n\nResearch within your program: both translating and modelling alignment problems into concrete empirical or theoretical form and executing on the resulting concrete problems. Execution means designing and running experimental protocols and methods for empirics, and proving, conjecturing, and investing in autoformalization for theory.\n\nWriting and presentation of completed research in the form of papers, blog posts, and talks.\n\nCommunication of research progress and obstacles to members of your team through channels like Slack on a daily basis and in weekly meetings.\n\nYou May Be a Good Fit If You\n\nHave a graduate degree (Ph.D.) or equivalent experience in a field relevant to the program you're applying to: ML, CS, mathematics, physics, statistics, philosophy, or something closely related\n\nHave a track record of research and strong technical writing ability: papers, preprints, or comparable output\n\nHave a strong mathematical background, even if your work is primarily empirical\n\nCan credibly articulate why your area of alignment work is worth pursuing\n\nAre willing to use AI tools aggressively in your own workflow, with appropriate care to not get fooled!\n\nAre motivated by alignment of artificial superintelligence (ASI) and want to contribute to it full-time\n\nStrong Candidates May Also Have\n\n*(For scalable oversight)* Hands‑on experience with debate, prover‑verifier games, RLHF empirics at frontier scale; familiarity with the scalable oversight literature; background in game theory, complexity theory, mechanism design, or decision theory\n\n*(For complexity theory)* Demonstrated expertise in theoretical computer science, including creative modeling and assumption generation\n\n*(For learning theory)* Background in algebraic geometry, Bayesian statistics, information theory, statistical physics, optimization theory, or learning theory; familiarity with (singular) learning theory\n\n*(Across programs)* Experience scaling experiments to billion‑plus parameter models; a track record of productive collaboration with engineers; prior engagement with our research or sibling organizations (Simplex, ARC, etc.). Experience with AI alignment is a plus, but is not a requirement: we are excited to bring experienced researchers from other fields into alignment work, and we see our ability to facilitate that transition as one of Sequent's comparative advantages.\n\nResearch Program Lead\nWe're also building out program leadership across our research areas. We expect to fill these roles primarily through targeted headhunting rather than open applications. If you think you might be a strong fit for a Research Program Lead position, a senior researcher who could direct an entire research program at Sequent, please reach out to us directly.\n\nApplication Process\nOur process starts with a short intro chat, followed by a take‑home assignment. Interviews will then discuss your take‑home and your research experience. We'll compensate you for the take‑home, subject to right‑to‑work constraints.\n\nLogistics\nSalary: Your salary depends on the scope, autonomy, and impact we expect you to have at Sequent. The expected range of in‑person salaries for this role is:\n\nL3: $236,000\n\nL4: $346,000\n\nL5: $451,000\n\nL6: $670,000\n\nL7: $930,000\n\nStrong early‑career researchers will typically come in at L3. Someone with several years of independent research experience would likely start at L4 or L5.\n\nLocation: Berkeley, California is strongly preferred; London is a secondary hub; remote may be considered in exceptional cases.\n\nBenefits:\n\n5 weeks of paid vacation per year, in addition to public holidays.\n\nComprehensive healthcare insurance (medical, dental, vision).\n\nUnlimited sick leave to prioritize your well‑being.\n\nAn unconditional 401(k) contribution equal to 4% of your salary.\n\nVisa sponsorship: We can sponsor visas for relocation to Berkeley.\n\nMinimum education: A bachelor's degree in a field relevant to the role, or an equivalent combination of education, training, and/or professional experience that demonstrates comparable knowledge.\n\nDeadline: Applications are rolling — we'll respond within one week of receipt.\n\nStart date: ASAP.\n\nCompensation Range: $236K - $930K\n\n#J-18808-Ljbffr","company":"Socket","rawCompany":"socket","city":"Berkeley","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-07-27T03:15:03.401Z","occupations":[{"code":"15-1221.00","title":"Computer and Information Research Scientists","slug":"computer-and-information-research-scientists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"19-1042.00","title":"Medical Scientists, Except Epidemiologists","slug":"medical-scientists-except-epidemiologists"}],"industries":[{"code":"541715","title":"Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology)","slug":"research-and-development-in-the-physical-engineering-and-life-sciences-except-nanotechnology-and-biotechnology"},{"code":"541690","title":"Other Scientific and Technical Consulting Services","slug":"other-scientific-and-technical-consulting-services"},{"code":"541990","title":"All Other Professional, Scientific, and Technical Services","slug":"all-other-professional-scientific-and-technical-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Research Scientist","description":"About Sequent\nSequent does research on how to align artificial superintelligence (ASI). ASI may be developed in the next few years, but it is unclear whether alignment is on track to be ready in the same timeframe. We aim at higher a priori confidence in aligned outcomes by pursuing a portfolio of theory and empirics bets, any one of which — if it succeeds — would meaningfully advance the field. We invest heavily in research automation to accelerate progress, and we believe that stronger alignment theory unlocks higher automation: more principled approaches give us better filters for which directions of automated research are promising.\n\nSequent was founded in May 2026 by researchers from UK AISI's Alignment Team, who ran the £30m Alignment Project, and Timaeus, who pioneered applying singular learning theory to alignment.\n\nFor more information, see our announcement.\n\nResearch at Sequent\nWe work across a portfolio of research areas, currently including:\n\nScalable oversight : empirical work on protocols (debate, recursive reward modeling, prover-verifier games) that allow weaker overseers to supervise stronger systems, paired with complexity-theoretic work on equilibria and reachability.\n\nComplexity theory : theoretical work on modeling the interaction of superintelligent agents with lower complexity training environments, including applications to scalable oversight, heuristic arguments, and other agendas.\n\nLearning theory : singular learning theory and its applications, deep learning theory, computational mechanics, etc.\n\nPersonas : theory and empirics of low-dimensional structure within model behavior across training and token dimensions.\n\nThe full set of bets has not yet been finalized but, in the future, may include further agendas like:\n\nHeuristic arguments : mechanistic understanding of what models know, low-probability estimation.\n\nGame theory : mechanism design, agent foundations, open-source game theory.\n\nA cross-cutting focus is research automation : building infrastructure and tooling to scale all of the above by leveraging AI research assistants at every level of the stack, across both theoretical and empirical work.\n\nAbout the Role\nAs a Research Scientist at Sequent, you'll work as part of a research program led by a senior researcher (which could be you!). We're hiring across our research programs, and the team you'd join depends on your strengths and interests — in your application, indicate which program(s) you'd be most interested in.\n\nOur goal of reaching higher confidence in alignment will require significant new contributions in both methods and scientific understanding. The work you do day-to-day will be part of a collaborative research program; all programs involve deep technical research, with strong support for cross-program collaboration. We welcome both empirical and theoretical profiles.\n\nResponsibilities\n\nResearch within your program: both translating and modelling alignment problems into concrete empirical or theoretical form and executing on the resulting concrete problems. Execution means designing and running experimental protocols and methods for empirics, and proving, conjecturing, and investing in autoformalization for theory.\n\nWriting and presentation of completed research in the form of papers, blog posts, and talks.\n\nCommunication of research progress and obstacles to members of your team through channels like Slack on a daily basis and in weekly meetings.\n\nYou May Be a Good Fit If You\n\nHave a graduate degree (Ph.D.) or equivalent experience in a field relevant to the program you're applying to: ML, CS, mathematics, physics, statistics, philosophy, or something closely related\n\nHave a track record of research and strong technical writing ability: papers, preprints, or comparable output\n\nHave a strong mathematical background, even if your work is primarily empirical\n\nCan credibly articulate why your area of alignment work is worth pursuing\n\nAre willing to use AI tools aggressively in your own workflow, with appropriate care to not get fooled!\n\nAre motivated by alignment of artificial superintelligence (ASI) and want to contribute to it full-time\n\nStrong Candidates May Also Have\n\n*(For scalable oversight)* Hands‑on experience with debate, prover‑verifier games, RLHF empirics at frontier scale; familiarity with the scalable oversight literature; background in game theory, complexity theory, mechanism design, or decision theory\n\n*(For complexity theory)* Demonstrated expertise in theoretical computer science, including creative modeling and assumption generation\n\n*(For learning theory)* Background in algebraic geometry, Bayesian statistics, information theory, statistical physics, optimization theory, or learning theory; familiarity with (singular) learning theory\n\n*(Across programs)* Experience scaling experiments to billion‑plus parameter models; a track record of productive collaboration with engineers; prior engagement with our research or sibling organizations (Simplex, ARC, etc.). Experience with AI alignment is a plus, but is not a requirement: we are excited to bring experienced researchers from other fields into alignment work, and we see our ability to facilitate that transition as one of Sequent's comparative advantages.\n\nResearch Program Lead\nWe're also building out program leadership across our research areas. We expect to fill these roles primarily through targeted headhunting rather than open applications. If you think you might be a strong fit for a Research Program Lead position, a senior researcher who could direct an entire research program at Sequent, please reach out to us directly.\n\nApplication Process\nOur process starts with a short intro chat, followed by a take‑home assignment. Interviews will then discuss your take‑home and your research experience. We'll compensate you for the take‑home, subject to right‑to‑work constraints.\n\nLogistics\nSalary: Your salary depends on the scope, autonomy, and impact we expect you to have at Sequent. The expected range of in‑person salaries for this role is:\n\nL3: $236,000\n\nL4: $346,000\n\nL5: $451,000\n\nL6: $670,000\n\nL7: $930,000\n\nStrong early‑career researchers will typically come in at L3. Someone with several years of independent research experience would likely start at L4 or L5.\n\nLocation: Berkeley, California is strongly preferred; London is a secondary hub; remote may be considered in exceptional cases.\n\nBenefits:\n\n5 weeks of paid vacation per year, in addition to public holidays.\n\nComprehensive healthcare insurance (medical, dental, vision).\n\nUnlimited sick leave to prioritize your well‑being.\n\nAn unconditional 401(k) contribution equal to 4% of your salary.\n\nVisa sponsorship: We can sponsor visas for relocation to Berkeley.\n\nMinimum education: A bachelor's degree in a field relevant to the role, or an equivalent combination of education, training, and/or professional experience that demonstrates comparable knowledge.\n\nDeadline: Applications are rolling — we'll respond within one week of receipt.\n\nStart date: ASAP.\n\nCompensation Range: $236K - $930K\n\n#J-18808-Ljbffr","datePosted":"2026-07-27T03:15:03.401Z","dateModified":"2026-07-27T03:15:03.401Z","hiringOrganization":{"@type":"Organization","name":"Socket","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Berkeley","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"d64924da1bb5c5f1584301a0"},"url":"https://jobsearcher.com/jobs/d64924da1bb5c5f1584301a0"}}