# Agent Post-Training, Connectors Research
**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA
**Salary:** $295K-$445K
**Experience:** 7+ years
**Skills:** Machine Learning, Software Engineering, Reinforcement Learning, RLHF, Rlaif, Post-Training, Evaluations, Graders, Synthetic Data, Model Training, LLMs, Coding Agents, Tool-Using Agents, Production Ml Systems, Data Pipelines
**Posted:** 2026-06-26
> Train frontier agents to interface with professional software via code, APIs, and structured integrations. Design experiments, own post-training improvements (RL, evals, data), and ship capabilities into major model runs.
## Job Description
## 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.

## Requirements
- 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.
- 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.
- 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. 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.
- 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.
**Apply:** https://hotfix.jobs/jobs/agent-post-training-connectors-research-at-openai-4b4892a7-347a-4c84-8afa-85da0d14c1c9
**Canonical:** https://hotfix.jobs/jobs/agent-post-training-connectors-research-at-openai-4b4892a7-347a-4c84-8afa-85da0d14c1c9