Research Engineer

About Sequent

Sequent 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.

Sequent was founded in 2026 by researchers from UK AISI's Alignment Project, and Timaeus, who pioneered applying singular learning theory to alignment.

For more information, see our announcement.

We are 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.

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.

• (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!

• 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.

• 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

Initial screener. A 20-minute conversation with a senior researcher to discuss your research background, motivations, and interests.

Work trial. A 4-hour take-home assignment. We'll collect references in parallel. We'll compensate you for your time, subject to right-to-work constraints.

Work trial review. A 45-minute discussion of your work trial.

Research talk. A…