Research Engineer

Resolution•Berkeley, CA

4d•Remote

About The Position

Resolution conducts research focused on aligning artificial superintelligence (ASI), aiming to ensure alignment is ready within the same timeframe ASI might be developed. The organization pursues a diverse portfolio of theoretical and empirical research bets, heavily investing in research automation to accelerate progress. They believe that stronger alignment theory leads to better automation by providing superior filters for promising research directions. Resolution was founded in 2026 by researchers from UK AISI's Alignment Team and Timaeus. The team is hiring Research Engineers across several focus areas, including Research Automation and Program-embedded Research Engineering. Research Engineers are integral to the research teams, driving both research and its underlying infrastructure, with a belief that clean engineering in automation, experimentation, and infrastructure is crucial for ambitious research.

Requirements

Strong software engineering background, including production-quality Python.
Deep experience with ML frameworks (PyTorch or Jax) and distributed-training stacks.
Demonstrated ability to ship complex systems end-to-end.
Bachelor's degree or equivalent in CS, physics, math, ML, or related.
Willing to use AI tools aggressively in your own workflow, with appropriate care to not get fooled!
Motivated by alignment of artificial superintelligence (ASI) and want to contribute to it full-time.

Nice To Haves

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.

Responsibilities

Build agentic research infrastructure: experiment orchestration, hypothesis generation, automated analysis pipelines; autoformalization tooling for the theory side; internal AI-powered tools for researchers.
Scale program experiments to frontier-tier models; build program-specific infrastructure; partner with researchers on engineering and implementation.
Maintain and extend distributed training, experiment, and evaluation infrastructure.
Contribute to and maintain shared codebases across the org.
Communication of engineering & automation progress, obstacles & learnings to your team and the wider org via Slack and in weekly meetings.