Agent Post-Training, Connectors Research

United States Digital Space LLC; San Francisco (CA); On-site; USD 100,000 - 130,000; Full time

Benefits offered by this job

Equal opportunity employer

Reasonable accommodation for disabilities

Job summary

United States Digital Space LLC is hiring for a role that empowers agents to interface with major productivity tools through coding. The position demands a blend of research taste and engineering execution to develop impactful solutions.

Candidates should possess a robust technical foundation in machine learning and experience with LLMs. This role offers an opportunity to contribute significantly to the training of next-gen agents while working with diverse teams in a high-agency environment.

Qualifications

• Strong technical fundamentals in machine learning, software engineering, or systems.
• Hands-on experience with LLMs and production ML systems.
• Excitement for open-ended problems and their solutions.

Responsibilities

• Design and run experiments for agent model improvement.
• Own improvements for the post-training stack.
• Work on training methodologies and product integrations.

Skills

Machine learning fundamentals

Software engineering

Systems knowledge

Hands-on experience with LLMs

Education

Degree in a related field

Tools

Codex

ChatGPT

API integration

Job description

About the Team

The Agent Post-Training team creates the frontier agents the company ships to the world. We train 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 member of Agent Post‑Training, Connectors, you will teach models how to interface with the top professional software using code. You will help train agents to use code, APIs, tools, and structured integrations to operate across applications like Slack, Google Workspace, GitHub, Notion, Linear, Salesforce, and other core systems of work. You will help enable models to take useful actions across a user’s digital context: finding information, updating systems, coordinating work, generating artifacts, and completing multi‑step workflows through the tools teams already use.

You will train models to be super‑charged by the world’s most important productivity and enterprise software, turning connected tools into a powerful action surface for our agents. 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

• Design and run experiments that improve agentic model behavior for complex software and plugins.
• Own end‑to‑end improvements to the post‑training stack, including RL, data pipelines, graders, reward signals, evals, diagnostics, and model‑behavior analysis.
• Build evals and environments that expose the next set of model failures, then turn those failures into training data, product fixes, or new research directions.
• Partner with Codex and ChatGPT product teams to understand what users need and translate product signal into model improvements.
• Work on early‑training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.
• Help decide which integrations, capabilities, and fixes are ready for inclusion in major model runs.
• Improve the machinery for large‑scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.
• Take on cross‑functional projects that touch model training, product infrastructure, and the production agent harness, such as multi‑agent systems or training directly against production‑like environments.
• Debug hard failures in shipped or near‑shipped models and turn messy qualitative behavior into concrete hypotheses, experiments, and fixes.

Qualifications

• 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.

Equal Opportunity and Legal Notice

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.

Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US‑based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non‑public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.

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