# Researcher, Alignment Oversight **Company**: OpenAI **Location**: San Francisco **Work arrangement**: hybrid **Experience**: mid **Job type**: Full time **Salary**: $250K – $445K **Category**: Engineering **Industry**: Technology **Wikidata**: https://www.wikidata.org/wiki/Q124605186 **Apply**: https://jobs.ashbyhq.com/openai/16ae0f82-e390-453e-b175-0655f1b0fc67?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply **Canonical**: https://yubhub.co/jobs/job_8b0ae9b6-96d ## Description As a researcher on the Alignment team, you will design and run experiments that improve our ability to oversee increasingly capable models. You will work on hands-on model training, evaluation design, and research infrastructure, and translating promising oversight ideas into systems that can operate on real model traffic and real user workflows. This role combines longer-horizon research with shorter deployment sprints, with projects typically scoped around 3-6 month research timelines and aimed at directly improving future model behavior. In this role, you will: - Design and implement alignment experiments focused on oversight systems for increasingly agentic AI models. - Deploy practical systems for action monitoring, red-teaming, and human-in-the-loop control. - Develop evaluations for alignment failure modes of the frontier models such as overeagerness, instruction following failures, covert actions, avoiding restrictions and scheming propensity. - Analyze deployment data to understand model failures, oversight gaps, and opportunities for training more aligned models. - Develop techniques for feeding oversight signals back into training while preserving the reliability and independence of the oversight process. - Produce externally publishable research when results advance the broader science of alignment. - Collaborate across research, product, security, safety, and engineering teams to turn alignment ideas into working systems. You might thrive in this role if you: - Have strong hands-on experience training, evaluating, or debugging large ML models, especially LLMs. - Have experience with reinforcement learning, post-training, preference optimization, scalable oversight, model evaluation, or adjacent empirical ML research. - Have strong engineering execution and can turn ambiguous research ideas into reliable experiments, tools, training pipelines, and production-facing systems. - Have research intuitions for what experiments are likely to teach us something, while staying grounded in implementation details and empirical results. - Are a team player - willing to do a variety of tasks that move the team forward. - Enjoy fast-paced, collaborative research environments where priorities shift as models and evidence change. - See safety and usefulness as coupled goals. ## Skills ### Required - ML models - reinforcement learning - post-training - preference optimization - scalable oversight - model evaluation ### Nice to have - LLMs - empirical ML research - engineering execution - research intuitions - team player --- Source: [Apply at jobs.ashbyhq.com](https://jobs.ashbyhq.com/openai/16ae0f82-e390-453e-b175-0655f1b0fc67?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)