Research Engineer, Frontier Evals & Environments

OpenAI

San Francisco, USonsite$205k-$380k/yrPosted Apr 13, 2025
Posting intelligenceMay be filled, listed long ago

Skills

hypothesisopenaiasanagoml

About the role

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 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 https://openai.com/index/gdpval/, SWE-bench Verified https://openai.com/index/introducing-swe-bench-verified/, MLE-bench https://openai.com/index/mle-bench/, PaperBench https://openai.com/index/paperbench/, and SWE-Lancer https://openai.com/index/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, enterprises, researchers, and everyday users.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity.

To notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=57018692298241&k=5MqR40fZd7jlxVUh5J-UeA. No response will be provided to inquiries unrelated to job posting compliance.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg&d=57018692298241.

OpenAI Global Applicant Privacy Policy https://cdn.openai.com/policies/global-employee-and-contractor-privacy-policy.pdf

At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.

Compensation

This Research Engineer role pays $205k-$380k/yr. Within typical range for research engineer roles in United States.

Questions about this role

Click "Apply with AI Applyd" above and you are done. Your resume is rewritten for this advert, the screening questions are answered, and it is submitted on OpenAI's own hiring system. No retyping your history, no fourteen tabs, no evening lost.

Compensation for Research Engineer roles in United States varies widely by seniority, employer size, and remote vs onsite arrangement. Check the salary range on this listing when published, or browse our Research Engineer hub for United States medians across recent openings.

You never touch the form - the application is filled and submitted for you on OpenAI's own hiring system. It is not marked sent when we press submit. It is marked sent when a confirmation from their system arrives at the address we apply with, and your dashboard shows which stage each application is at until then.

Twelve applicant tracking systems have a real apply path: Workday, Greenhouse, Lever, Ashby, Workable, iCIMS, Personio, Recruitee, Teamtailor, Rippling, Breezy and SmartRecruiters. Your application goes in on the employer's own hiring system, never into an aggregator queue.

Want AI Applyd to auto-apply to roles like this?

We tailor your resume per posting, fill the forms, and track replies for you.