Agent Post-Training, Frontier Evals and Environments Research

United States Digital Space LLC; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

United States Digital Space LLC is seeking a researcher focused on building environments for frontier evaluations that drive progress towards safe AGI/ASI. You will guide ambitious training runs with prominent industry impact.

The ideal candidate has a strong foundation in machine learning and experience with modern methods. Join us to help shape the future of artificial intelligence development and deployment.

Qualifications

• Strong technical fundamentals in machine learning, software engineering, and related fields.
• Hands-on experience with LLMs, RL and related methodologies.
• Ability to navigate vague problems to define concrete experiments.

Responsibilities

• Create ambitious RL environments for model evaluation.
• Develop methodologies for exploring model behavior.
• Steer training for major runs and evaluate outcomes.

Skills

Machine learning

Software engineering

Systems

Statistics

RLHF/RLAIF

Model training

Education

Degree in relevant field

Tools

LLMs

Synthetic data

Job description

About the Team

The Agent Post‑Training team creates the frontier agents the company 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 the company’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 the company. Some prior open‑source 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.

Responsibilities

• 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.

Qualifications

• Have strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field, and can learn quickly across 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 the Company

the company 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.

Equal Opportunity Employment

We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. For additional information, please see the company’s affirmative action and equal employment opportunity policy statement.

We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.

Privacy Notice

the company Global Applicant Privacy Policy