About Sequent
Sequent is a new AI alignment research organization, founded in 2026 by researchers from the UK AISI’s Alignment Team and Timaeus. 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 theory unlocks higher automation: more principled approaches give us better filters for which directions of automated research are promising.
For more information, see our announcement.
About Our Research
We work across a portfolio of research areas, currently including:
- Scalable 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.
- Complexity 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.
- Learning theory: singular learning theory and its applications, deep learning theory, computational mechanics, etc.
- Personas: theory and empirics of low-dimensional structure within model behavior across training and token dimensions.
The full set of bets has not yet been finalized but, in the future, may include further agendas like:
- Heuristic arguments: mechanistic understanding of what models know, low-probability estimation.
- Game theory: mechanism design, agent foundations, open-source game theory.
A 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.
About the Team
We’ll soon be hiring Research Engineers across several main focus areas. We expect the boundaries between these areas to be flexible, but please indicate which mode you’re more interested in (or “either”) in your application.
Research Automation (primary focus). A cross-cutting function that builds the infrastructure and tooling our researchers use to scale their work, increasingly leveraging fleets of AI research assistants alongside small teams of humans.
Program-embedded Research Engineering (also hiring). Research engineers embedded within one of our research programs (scalable oversight, complexity theory, learning theory, personas, and possible future programs like heuristic arguments or game theory), partnering with researchers on scaling experiments, building program-specific infrastructure, and translating theoretical insights into empirical tools.
About the Role
Research Engineers at Sequent are core members of our research teams, directly driving both research and the core infrastructure behind it. We believe clean engineering on automation, experimentation, and infra is essential to ambitious research, and that excellence on this front requires active research participation.
Responsibilities
- (Research automation track) Build agentic research infrastructure: experiment orchestration, hypothesis generation, automated analysis pipelines; autoformalization tooling for the theory side; internal AI-powered tools for researchers
- (Program-embedded track) Scale program experiments to frontier-tier models; build program-specific infrastructure; partner with researchers on engineering and implementation.
- (Both) Maintain and extend distributed training, experiment, and evaluation infrastructure
- (Both) Contribute to and maintain shared codebases across the org
- (Both) Communication of engineering & automation progress, obstacles & learnings to your team and the wider org via Slack and in weekly meetings.
We’re on the lookout for excellence, so if you’re a cracked engineer who doesn’t precisely fit these descriptions, please still apply!
You May Be a Good Fit If You
- Have a strong software engineering background, including production-quality Python
- Have deep experience with ML frameworks (PyTorch or Jax) and distributed-training stacks
- Have a demonstrated ability to ship complex systems end-to-end
- Have a Bachelor’s degree or equivalent in CS, physics, math, ML, or related
- Are willing to use AI tools aggressively in your own workflow, with appropriate care to not get fooled!
- Are motivated by alignment of artificial superintelligence (ASI) and want to contribute to it full-time.
Strong Candidates May Also Have
- Experience with autoformalization, Lean, or other proof-assistant tooling
- Background in research infrastructure or ML platform engineering at frontier labs
- Experience scaling ML systems to 100B+ parameter scale
- Experience with CUDA kernel development or GPU optimization
- Familiarity with alignment research
Logistics
- Location: Berkeley strongly encouraged; London is a secondary hub; remote may be considered in exceptional cases.
- Visa sponsorship: Yes, for relocation to Berkeley
- Start date: Rolling
Expression of Interest
We expect to open a full hiring round soon. In the meantime, if you’re interested in roles at Sequent, please fill in this Expression of Interest form.