Researcher, Context - Agent Post-Training
WFA Digital Insight
Demand for AI and machine learning specialists has surged in recent years, with a notable 25% increase in job postings for research roles in 2025. As companies like Openai continue to push the boundaries of artificial intelligence, the need for skilled researchers who can develop and train models has become paramount. With the rise of remote work, opportunities for talented professionals to contribute to cutting-edge projects have expanded. Before applying, candidates should be aware of the high level of technical expertise required for this role and the importance of staying up-to-date with the latest developments in machine learning and AI.
Job Description
About the Role
The Researcher, Context - Agent Post-Training role at Openai is a unique opportunity to contribute to the development of frontier agents that will shape the future of artificial intelligence. As a member of the Agent Post-Training team, you will be responsible for designing and running experiments that improve the scaling of compute on context, owns end-to-end improvements to the post-training stack, and partnering with product teams to translate product signal into model improvements.The Agent Post-Training team is at the forefront of creating the next generation of agents that can operate computers, collaborate with people and other agents, and expand what people and organizations can imagine, attempt, and achieve. As a researcher on this team, you will have the opportunity to work on open-ended problems where the path is unclear, and the signal is noisy, requiring both research taste and engineering execution.
What You Will Do
- Design and run experiments that improve scaling of compute on context
- 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
What We Are Looking For
- Strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field
- 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
- Ability to learn quickly across different areas of expertise
- Experience working on open-ended problems where the path is unclear, and the signal is noisy
- Strong opinions about what makes an agent useful, reliable, honest, tasteful, and easy to work with
- Ability to 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
- Comfort working across research, product, infrastructure, and data teams
Nice to Have
- Experience with multi-agent systems or training directly against production-like environments
- Knowledge of Codex and ChatGPT product teams and their requirements
- Familiarity with the latest developments in machine learning and AI
Benefits and Perks
- Opportunity to work on cutting-edge projects in AI and machine learning
- Collaborative and dynamic work environment
- Professional development opportunities
- Flexible remote work arrangements
- Access to the latest tools and technologies
- Competitive compensation and benefits package
- Equity and stock options
- Health and wellness programs
- Generous paid time off and vacation days
How to Stand Out
- Make sure to highlight your experience with machine learning, software engineering, and systems in your resume and cover letter.
- Prepare to talk about your experience with LLMs, RL, and other relevant technologies during the interview process.
- Showcase your ability to learn quickly and work on open-ended problems by providing specific examples from your past experience.
- Be prepared to discuss your opinions on what makes an agent useful, reliable, honest, tasteful, and easy to work with.
- Emphasize your ability to work across different teams, including research, product, infrastructure, and data teams.
- Consider creating a portfolio that showcases your work on machine learning and AI projects, including any relevant code or research papers.
- During salary negotiations, be prepared to discuss your expectations and requirements, and be open to creative solutions that meet your needs.
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