Agent Post-Training, API & Power Users

Details

The Agent Post-Training team builds the frontier agents OpenAI ships to the world. The team defines next-generation agent capabilities, builds the training signal that teaches those abilities, and runs experiments that carry capabilities through major training runs into products. Work spans coding, tool use, computer use, multi-agent coordination, long-horizon execution, factuality, instruction following, calibrated reasoning, and taste.

Role overview

As a member of the API & power-users team you will improve the capabilities, reliability, and product fit of OpenAI’s agentic models for power users and API developers. You might design evals from real developer workflows, build training environments around production-like tool use, turn qualitative model failures into training data/evals/post-training interventions, and drive behavior improvements from discovery through post-training, integration, and launch. You will work closely with researchers, engineers, API/product teams, Codex, infrastructure, and safety/alignment partners to choose behaviors to measure and train, and to carry improvements into major model runs.

Responsibilities

• Design and run experiments to improve model behavior in API and power-user workflows (function calling, tool use, coding, planning, long-horizon execution, factuality, instruction following, error recovery, calibrated reasoning).
• Build evals, graders, and environments from real developer and power-user workflows and convert observed failures into training data, model-behavior hypotheses, and shipped improvements.
• Partner with API and power-users to identify high-leverage behavior gaps and convert product signals into post-training interventions.
• Improve model behavior when composed into systems: reliable tool use, respecting developer intent, handling partial failures, clarifying when appropriate, and maintaining coherence across multi-step tasks.
• Own end-to-end model behavior projects from qualitative failure analysis through data generation, training experiments, eval design, integration into major runs, and launch readiness.
• Develop feedback loops using power-user traces, API usage patterns, and production-like environments to discover agentic model failures and gaps.
• Debug hard failures in shipped or near-shipped models by moving between traces, evals, training data, model outputs, and product context.
• Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and eval loops that shape downstream agent behavior.
• Improve machinery for large-scale training and launch: experiment velocity, reliability, observability, reproducibility, cost, latency, and production readiness.
• Take on cross-functional projects touching model training, product infrastructure, and the production agent harness (e.g., multi-agent systems or training against production-like environments).

Requirements / Qualifications

• Strong technical fundamentals in one or more of: machine learning, software engineering, systems, statistics, or applied research.
• Hands-on experience with LLMs and post-training techniques; experience with RL / RLHF / RLAIF, evals, graders, synthetic data, coding agents, tool-using agents, API products, or production ML systems is emphasized.
• Ability to analyze transcripts, traces, eval failures, or API interactions and form concrete hypotheses about what models need to learn.
• Comfortable working across research, product, infrastructure, data, evals, and safety boundaries and communicating clearly with each group.
• Experience designing experiments, generating training data, creating evals/graders, and carrying improvements through training and integration.

Benefits

• Base salary range: $295,000 – $445,000 (actual base pay may vary by market location, knowledge, skills, and experience).
• Total compensation may include equity and performance-related bonuses for eligible employees.
• Medical, dental, and vision insurance with employer contributions to Health Savings Accounts.
• Pre-tax Health FSA, Dependent Care FSA, and commuter expense accounts.
• 401(k) with employer match.
• Paid parental leave and additional paid medical and caregiver leave.
• Flexible PTO and paid company holidays and office closures.
• Mental health and wellness support; employer-paid basic life and disability coverage.
• Annual learning and development stipend; daily meals in offices and meal delivery credits as eligible.
• Relocation support for eligible employees.

About OpenAI

OpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. The company emphasizes safety, diverse perspectives, and inclusion and provides reasonable accommodations to applicants with disabilities.