The RolePlato is hiring an individual contributor researcher to design safety environments and benchmarks, starting with evaluations that measure how well AI models can improve themselves and other models.
You will establish a new research direction at Plato, designing safety-relevant benchmarks and working with engineers to turn them into runnable environments. Your initial focus will be measuring AI R&D capabilities, including experiment design, model training, and research replication.
You will also investigate reward hacking, sabotage, deception, and other misaligned behavior. The work spans research design, empirical evaluation, and failure analysis, with substantial ownership of the research agenda. Some environments may also be used in training to reduce the behaviors they reveal.
What You'll Do- Design Safety Benchmarks: Measure AI research automation and self-improvement. Define tasks, baselines, budgets, and success criteria that separate meaningful progress from superficial completion.
- Build Safety Environments: Design environments that expose reward hacking, sabotage, deception, and failures to follow intended constraints.
- Hold the Evaluation Bar: Investigate contamination, grader weaknesses, task ambiguity, and shortcuts that could make a result misleading.
- Track Frontier Capabilities: Run experiments with current models, inspect trajectories and failures, and measure how capabilities change across models and over time.
- Work with Engineering: Turn research designs into reproducible environments with clear scoring, useful instrumentation, and documented limitations.
- Share the Findings: Translate results into benchmark releases, research reports, and concrete recommendations for lab partners.
Required Qualifications- Recent Research: Hands-on work in frontier-model evaluation, benchmarks, or AI safety in the past one to two years.
- Research Track Record: Meaningful contributions to papers, released benchmarks, public reports, or comparable empirical work. We care about the quality of your work and your contribution more than credentials or years of experience.
- Current Model Knowledge: Practical familiarity with current frontier models and a specific, evidence-based view of their capabilities, limitations, and likely development.
- Experimental Judgment: You can explain what an evaluation measures, how it could be gamed, and which conclusions its results do and do not support.
- Research Ownership: Ability to turn an ambiguous question into a concrete experimental design and collaborate closely with engineers to implement it.
- Safety Focus: Interest in measuring safety-relevant capabilities and behavior, including the tradeoffs when evaluation environments are also used for training.
- Bay Area Based: Based in the San Francisco Bay Area, or willing to relocate.
Preferred Qualifications- Designed and released a benchmark, particularly for AI R&D, long-horizon agents, dangerous capabilities, or AI control. Relevant reference points include RE-Bench, MLE-bench, and PaperBench.
- Experience in an independent evaluation or safety organization, a frontier lab safety team, or pre-deployment testing with labs or government evaluation bodies.
- Direct research on models conducting ML experiments, training or improving other models, replicating papers, or completing extended research tasks.
- A software engineering background or programming skills that let you implement environments, evaluation harnesses, and analysis tools yourself.
- Experience publishing rigorous empirical research, including work at major machine-learning venues.
