Agent Post-Training, Artifacts 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 in San Francisco is seeking a skilled individual to join their Agent Post-Training team. This role focuses on training advanced AI models to create useful artifacts such as reports and dashboards. It involves significant collaboration with researchers, engineers, and product teams to refine model capabilities and improve user experience.

The ideal candidate should have a background in machine learning and experience with reinforcement learning. The company values diversity and is committed to equal opportunity in hiring.

Qualifications

• Strong technical fundamentals in ML, software engineering, statistics, or a related field.
• Hands-on experience with LLMs, RL, evals, and synthetic data.
• Ability to tackle open-ended problems and derive concrete experiments.

Responsibilities

• Design and run experiments to improve agent behavior.
• Own end-to-end improvements to the post-training stack.
• Collaborate with product teams to understand user needs.

Skills

Machine learning fundamentals

Software engineering

Statistics

Reinforcement learning experience

Experience with LLMs

Education

Degree in a related field

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 member of Agent Post-Training, Artifacts, you will train frontier models to create polished, useful work products: documents, spreadsheets, slide decks, dashboards, reports, analyses, and other interactive or editable artifacts. You will help teach our models to move from a vague user goal to a finished artifact with strong structure, visual taste, domain judgment, correctness, and low latency. This work will require owning improvements across our post-training stack, including RL, data pipelines, graders, reward signals, evals, and behavioral analysis.

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.

• 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.

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.
• Have some prior background in consulting, finance, marketing, operations, or data science.

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.

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.