Estimated Base Salary $295K – $445K

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
• 401(k) retirement plan with employer match
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend to fuel your professional growth
• Daily meals in our offices, and meal delivery credits as eligible
• Relocation support for eligible employees
• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Agent Post-Training team creates the frontier agents OpenAI 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 builds the data, environments, graders, training methods, and feedback loops that shape what OpenAI’s next agents can do and what they are like to work with, then carries those improvements through major training runs and into products used by people every day.

About the Role

As a member of the Agent Post-training Personality team, you will help make OpenAI’s agents exceptional collaborators. You will study what makes an agent thoughtful, clear, perceptive, appropriately proactive, and genuinely easy to work with, then translate those insights into evals, training data, reward signals, and model improvements.

We use “personality” to mean much more than writing style or general likability. It includes whether an agent understands what the user is trying to accomplish, communicates with good judgment, adapts to context, asks useful questions, handles disagreement honestly and takes initiative at the right moments. The goal is to create a strong, tasteful default that can adapt to different people and situations.

This work combines behavioral research, product thinking, research and communication taste. You will collaborate with product teams, human experts, and researchers across post-training and pretraining to ensure that improvements survive the full training stack and reach the models people use every day.

Responsibilities

• Develop a rigorous understanding of what makes an agent a great collaborator across professional, creative, technical, and everyday work.
• Turn qualitative judgments about model behavior into concrete hypotheses, evals, graders, and training interventions.
• Study explicit and implicit user signals to understand which behaviors create trust, satisfaction, continued use, and successful outcomes.
• Work with human experts and trainers to produce high-quality, tasteful rollouts and preference data that capture excellent collaborative behavior.
• Improve reward models and RL objectives for model behaviors.
• Work with pretraining and early-training teams on data mixtures, objectives, synthetic data, and other upstream choices that shape downstream personality.
• Build sustainable pipelines for updating older training data as our understanding of excellent model behavior evolves.
• Partner closely with ChatGPT, Codex, and other product teams to turn consumer insight into model improvements and validate them in real workflows.
• Own projects end to end, from observing a subtle behavioral failure through experimentation, training, evaluation, and launch.

Requirements

• Think instinctively from the user’s perspective and care deeply about how models feel to work with, not only how they perform on benchmarks.
• Can translate subjective-seeming product questions into falsifiable hypotheses and rigorous evaluations without losing the nuance that made the question important.
• Care about preserving individuality, adaptability, and behavioral diversity rather than optimizing every model toward one narrow style.
• Want to shape how frontier agents communicate, collaborate, and build trust with millions of people.
• Have strong technical foundations in machine learning, software engineering, statistics, behavioral science, HCI, or a related field, and can quickly learn across unfamiliar parts of the stack.
• Have strong taste for model behavior: you can look at user feedback and can explain why one response feels thoughtful, natural, and useful while another does not.
• Have experience with LLMs, post-training, RL/RLHF, reward modeling, evals, synthetic data, pretraining data, or production ML systems.
• Are excited by ambiguous capability problems where the signal is noisy, the failures are qualitative, and the solution may involve data, training, evals, product changes, or all of the above.
• Can work effectively with researchers, engineers, product teams, designers, domain experts, human-data teams 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.

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Estimated Base Salary $295K – $445K

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
• 401(k) retirement plan with employer match
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend to fuel your professional growth
• Daily meals in our offices, and meal delivery credits as eligible
• Relocation support for eligible employees
• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Agent Post-Training team creates the frontier agents OpenAI 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 OpenAI's next agents can do, then carry those capabilities through major training runs and into the products people use.

About the Role

As a researcher working on Frontier Evals & Environments, you will help build north star model environments to drive progress towards safe AGI/ASI. Your work will directly guide the research programs of the most ambitious training runs happening at OpenAI. Some prior open-sourced evaluations built by researchers in this role include GDPval, SWE-bench Verified, MLE-bench, PaperBench, and SWE-Lancer. If you are interested in feeling firsthand the fast progress of our models, and steering them towards good outcomes, this is the role for you.

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.

In this role, you might

• Create ambitious RL environments to push our models to their limits, and measure frontier model capabilities, skills, and behaviors
• Develop new methodologies for automatically exploring the behavior of these models
• Dive deep into the science of measurement, including understanding scalability, reliability, and variance of our evaluation methodology
• Help steer training for our largest training runs, and see the future first
• Design scalable systems and processes to support continuous evaluation
• Build self-improvement loops to automate model understanding

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.

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We are seeking an Agent Post-Training, Context Researcher to join our team at OpenAI. You will play a crucial role in scaling compute spent on context, working on the frontier training stack, and enabling the next paradigm of model training. This is a high-agency role for individuals who want their work to directly impact frontier models.

Responsibilities

• Design and run experiments to improve the scaling of compute on context.
• Own end-to-end improvements to the post-training stack, including reinforcement learning, data pipelines, graders, reward signals, evaluations, diagnostics, and model-behavior analysis.
• Develop evaluations and environments that expose the next set of model failures and turn those failures into training data, product fixes, or new research directions.
• Collaborate with Codex and ChatGPT product teams to understand user needs and translate product signal into model improvements.
• Work on early-training and alignment interventions, including data mixtures, objectives, synthetic data, and evaluation 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, focusing on 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.

Requirements

• Strong technical fundamentals in machine learning, software engineering, systems, statistics, or a related field.
• Hands-on experience with large language models, reinforcement learning, post-training, evaluations, graders, synthetic data, model training, coding agents, tool-using agents, or production machine learning systems.
• Experience working across research, product, infrastructure, data, evaluations, and safety boundaries.

Benefits

• Estimated base salary: $295K - $445K
• Generous equity and performance-related bonuses
• Medical, dental, and vision insurance for you and your family
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses
• 401(k) retirement plan with employer match
• Paid parental leave, medical and caregiver leave
• Flexible PTO and paid company holidays
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend
• Daily meals in our offices and meal delivery credits
• Relocation support for eligible employees

About OpenAI

OpenAI is committed to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of AI capabilities and seek to safely deploy them through our products.

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Estimated Base Salary $295K – $445K

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
• 401(k) retirement plan with employer match
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend to fuel your professional growth
• Daily meals in our offices, and meal delivery credits as eligible
• Relocation support for eligible employees
• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Agent Post-Training team creates the frontier agents OpenAI 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.

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.

In this role, you might

• 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.

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$158.4K – $320K • Offers Equity

The base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. If the role is non-exempt, overtime pay will be provided consistent with applicable laws. In addition to the salary range listed above, total compensation also includes generous equity, performance-related bonus(es) for eligible employees, and the following benefits.

• Medical, dental, and vision insurance for you and your family, with employer contributions to Health Savings Accounts
• Pre-tax accounts for Health FSA, Dependent Care FSA, and commuter expenses (parking and transit)
• 401(k) retirement plan with employer match
• Paid parental leave (up to 24 weeks for birth parents and 20 weeks for non-birthing parents), plus paid medical and caregiver leave (up to 8 weeks)
• Paid time off: flexible PTO for exempt employees and up to 15 days annually for non-exempt employees
• 13+ paid company holidays, and multiple paid coordinated company office closures throughout the year for focus and recharge, plus paid sick or safe time (1 hour per 30 hours worked, or more, as required by applicable state or local law)
• Mental health and wellness support
• Employer-paid basic life and disability coverage
• Annual learning and development stipend to fuel your professional growth
• Daily meals in our offices, and meal delivery credits as eligible
• Relocation support for eligible employees
• Additional taxable fringe benefits, such as charitable donation matching and wellness stipends, may also be provided.

About the Team

The Intelligence and Investigations team seeks to rapidly identify and mitigate abuse and strategic risks to ensure a safe online ecosystem in close collaboration with our internal and external partners. Our efforts contribute to OpenAI's overarching goal of developing AI that benefits humanity.

About the Role

We are looking for an expert in biological and chemical risks to lead red teaming efforts for biosafety and to help mature OpenAI’s bio bounty programs. You will help shape biology-relevant test plans, guide external and internal engagements, ensure our testing is impactful, and translate findings into clear outcomes. In addition, you’ll play a critical role in driving our bio bounty programs, including managing external researcher relationships, evolving challenge questions, reviewing submissions that require biological subject-matter expertise, and maturing the program as it continues to scale.

Responsibilities

• Lead bio red teaming work from a domain perspective, including shaping evaluation questions, scenarios, and success criteria for biology-relevant misuse pathways.
• Review and pressure-test bio red teaming outputs, assess report quality, and help translate findings into concise risk calls and recommended next steps.
• Identify new red teaming engagements or external expert collaborations that would improve OpenAI's visibility into biological and overall CBRN misuse risks.
• Bring an adversarial mindset to assess how malicious actors could attempt to misuse model capabilities across planning, protocol design, procurement, troubleshooting, and operationalization.
• Directly manage Bio Bounty program strategy and maturity, including challenge-question lifecycle, expert researcher pipeline, submission review standards, and program health.
• Partner with operations owners on program execution while providing subject-matter review where biological expertise is required.
• Review Bio Bounty applications and invite known experts when appropriate to strengthen the researcher pool.
• Lead final biological-risk review on eligible submissions before awards, including severity, impact, novelty, and mitigation.
• Lead cross-functional efforts with Preparedness, Safety Systems, Policy, Legal, and other teams to turn high-quality findings into concrete follow-up work.
• Communicate findings clearly to technical and non-technical stakeholders, including concise briefings for leadership when needed.

Requirements

• Have a technical/scientific background with at least 8 years of technical CBRN experience
• Have deep subject-matter expertise in biosafety, biosecurity, biodefense, synthetic biology, microbiology, public health security, or adjacent CBRN domains, ideally with experience assessing dual-use biological risks.
• Have experience with bio red teaming, threat modeling, misuse analysis, adversarial testing, intelligence analysis, or professional casework involving malicious actors or weaponization pathways.
• Bring strong operations and/or program management experience.
• Can prioritize ambiguous findings based on severity, feasibility, novelty, exposure, and potential real-world impact.
• Have excellent judgment when handling sensitive biological information and can separate legitimate research from content that may enable harm.
• Communicate complex technical and risk judgments in clear, grounded language for product, policy, safety, legal, and leadership audiences.
• Are comfortable working cross-functionally in fast-changing environments where the right answer may require both technical depth and pragmatic execution.
• Have enough technical fluency to partner with data, engineering, and safety teams on signals, tooling, and scalable review processes.
• Are resilient and thoughtful when engaging with sensitive, high-stakes material.

Nice to have

• Prior experience managing a bounty, vulnerability disclosure, external researcher, or expert review program.
• Experience with AI safety evaluations, model red teaming, or frontier model risk assessment.
• Familiarity with how advanced AI systems may affect biological safety, biosecurity, or CBRN risk.

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