OpenAI· Agents· San Francisco
Research Engineer, Frontier Evals & Environments
Comp$205K – $380K
Classified Tasks (22)
Automate 0%Augment 73%Human-Only 27%
Augment (16)
AI assists, human decides
Build training signals that teach desired agent abilities
technical
Run experiments to validate and realize proposed agent capabilities
operational
Create ambitious reinforcement learning (RL) environments to stress-test models
technical
Build evaluation data sets for training and measurement
technical
Develop and implement graders for automated evaluation
technical
Build feedback loops that incorporate evaluation results into training
technical
Integrate developed capabilities into major model training runs
operational
Ship improvements and capability updates into user-facing products
operational
Measure and quantify whether model changes achieved intended outcomes
analytical
Analyze scalability, reliability, and variance of evaluation methodologies
analytical
Design scalable systems and processes to support continuous evaluation
operational
Build self-improvement loops to automate model understanding
technical
Build experiment and evaluation pipelines to run behavioral tests
technical
Execute model runs and controlled experiments against evaluation environments
operational
Analyze experimental results to assess model performance and behaviors
analytical
Formulate and implement follow-up actions and iterative improvements based on analysis
operational
Human-Only (6)
Requires human judgment
Define target capabilities and evaluation goals for next-generation agents
leadership
Design and develop training methods for agent behaviors
technical
Collaborate with researchers, engineers, product, infrastructure, and safety/alignment teams to define model-run content
communication
Develop new methodologies for automated exploration of model behavior
technical
Guide and steer training decisions for large-scale training runs
leadership
Define hypotheses for behavioral experiments based on vague problem statements
analytical
Job description
Research Engineer, Frontier Evals & Environments | OpenAI Careers ## Research Engineer, Frontier Evals & Environments Agents - San Francisco Apply now(opens in a new window) **About the team** The Agent Post-Training team creates the frontier agents OpenAI ships to the world. We are training the models behind our agents in Codex, ChatGPT, the API, and other frontier products: persistent, proactive intelligence that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. We define what the next generation of agents should be able to do, build the training signal that teaches those abilities, and run the experiments that make them real. Our work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste. Our team is where new model capabilities get made. We build the data, environments, graders, training methods, and feedback loops that shape what OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use. **About the Role** As a researcher working on Frontier Evals & Environments, you will help build north star model environments to drive progress towards safe AGI/ASI. Your work will directly guide the research programs of the most ambitious training runs happening at OpenAI. Some prior open-sourced evaluations built by researchers in this role include GDPval, SWE-bench Verified, MLE-bench, PaperBench, and SWE-Lancer. If you are interested in feeling firsthand the fast progress of our models, and steering them towards good outcomes, this is the role for you. You will work with researchers, engineers, product teams, infrastructure teams, and safety/alignment partners to decide what should go into major model runs, measure whether it worked, and ship improvements into products used by real people. This is a high-agency role for people who want their work to land directly in frontier models. **In this role, you'll:** * Create ambitious RL environments to push our models to their limits, and measure frontier model capabilities, skills, and behaviors * Develop new methodologies for automatically exploring the behavior of these models * Dive deep into the science of measurement, including understanding scalability, reliability, and variance of our evaluation methodology * Help steer training for our largest training runs, and see the future first * Design scalable systems and processes to support continuous evaluation * Build self-improvement loops to automate model understanding **You might thrive in this role if you** * Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across the parts you have not worked in before. * Have hands-on experience with LLMs, RL, RLHF/RLAIF, post-training, evals, graders, synthetic data, model training, coding agents, tool-using agents, or production ML systems. * Are excited by open-ended problems where the path is unclear, the signal is noisy, and the right answer requires both research taste and engineering execution. * Care about product impact and model behavior, not just benchmark movement. You have opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with. * Can move from a vague behavioral problem to a concrete experiment: define the hypothesis, build the pipeline, run the model, analyze the result, and decide what to do next. * Are comfortable working across research, product, infrastructure, data, evals, and safety boundaries, and can communicate clearly with each group. * Like building load-bearing systems and processes when that is what the team needs, even if the work is not glamorous. * Want to train and ship the models that make agents genuinely useful for developers, ente