Research Engineer / Scientist, Alignment Science
Conducts experimental ML research on AI alignment and safety for powerful systems, focusing on scalable oversight, control, and stress-testing. Requires strong software/ML engineering, empirical research experience, and Python proficiency.
About the job
Representative projects
- Testing robustness of safety techniques by training models to subvert interventions.
- Running multi-agent reinforcement learning experiments for techniques like AI Debate.
- Building tooling to evaluate LLM-generated jailbreaks.
- Writing scripts and prompts for safety-relevant reasoning evaluations.
- Contributing to research papers, blog posts, and talks.
- Running experiments for Responsible Scaling Policy implementation.
You may be a good fit if you
- Have significant software, ML, or research engineering experience.
- Have experience contributing to empirical AI research projects.
- Have familiarity with technical AI safety research.
- Prefer fast-moving collaborative projects.
- Pick up slack beyond your job description.
- Care about AI impacts.
Strong candidates may also
- Have experience authoring ML, NLP, or AI safety research papers.
- Have experience with LLMs.
- Have experience with reinforcement learning.
- Have experience with Kubernetes clusters and complex shared codebases.
Note: Interviews conducted in Python; Bay Area base preferred.
Logistics
Education: Bachelor's degree in related field or equivalent experience. Location: Hybrid policy - in office at least 25% of time.
Skills
Python, Machine Learning, LLMs, Reinforcement Learning, Kubernetes, Ai Safety, Scalable Oversight, Ai Control, Alignment Stress-Testing, Research Engineering
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