Why This Job is Featured on The SaaS Jobs
This role sits at the intersection of research and production, which is increasingly central to modern SaaS as AI capabilities are shipped as continuously improving services rather than static releases. The remit spans training, evaluation, and deployment integration, reflecting how leading software companies now treat model behavior, reliability, and safety as product-level concerns that must be engineered and measured.
For a SaaS career, the standout value is sustained exposure to the full lifecycle of an AI feature once it becomes part of a live platform. Work on evaluation design, monitoring, and inference-time interventions maps closely to how SaaS organizations manage quality in the wild, including drift, edge cases, and user-driven adversarial pressure. The emphasis on quantitative methods and reinforcement learning also builds a toolkit that transfers across AI-enabled SaaS teams working on personalization, copilots, search, and automation.
The role is best suited to professionals who like turning ambiguous questions into testable hypotheses and can alternate between rigorous experiments and practical infrastructure work. It will fit someone comfortable operating with independence while collaborating across research, engineering, and product-adjacent functions, and who is motivated by making model behavior more dependable in real deployments.
The section above is editorial commentary from The SaaS Jobs, provided to help SaaS professionals understand the role in a broader industry context.
Job Description
About the Team
The Alignment Science team at OpenAI studies the science of intent alignment: how to train models to understand what users are actually asking for, act faithfully on that intent while respecting safety constraints, verify what they did, and report their limitations honestly. Our work sits alongside broader value alignment efforts, but this team focuses on scalable methods for ensuring instruction-following, honesty, and robustness as models become more capable.
We work on both sides of alignment research: producing externally publishable results and integrating promising techniques into the models OpenAI deploys. Recent team research on model confessions studies how models can be trained to honestly report shortcomings after their original answer, including failures involving hallucination, instruction following, scheming, and reward hacking. That work reflects a broader agenda: build scalable and general methods to ensure models follow human intent.
The team uses a mix of training and evaluation methods, with a focus on reinforcement learning. We care about rigorous, quantitative research that can translate into safer model behavior.
About the Role
As a Research Engineer / Research Scientist on the Alignment team, you will design and run experiments that help increasingly capable models follow user intent, remain calibrated about correctness and risk, and honestly surface their own mistakes. You will work on hands-on model training, evaluation design, and research infrastructure, while helping turn promising alignment methods into techniques that can be used in frontier model development.
This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees. We are also open to exceptional remote candidates who can operate independently and collaborate closely with the team.
In this role, you will:
Design and implement alignment experiments focused on intent following, honesty, calibration, and robustness.
Train and evaluate models using reinforcement learning, and other empirical ML methods.
Develop evaluations for failure modes such as hallucination, instruction-following failures, reward hacking, covert actions, and scheming.
Study methods that encourage models to verify their behavior and report shortcomings honestly, including confession-style training objectives.
Build monitoring and inference-time interventions that ensure compliant behavior or surface model issues to users or downstream systems.
Investigate how alignment methods scale with model capability, compute, data, context length, action length, and adversarial pressure.
Integrate successful techniques into model training and deployment workflows.
Produce externally publishable research when results advance the broader science of alignment.
Collaborate with researchers and engineers across post-training, RL, evaluations, safety, and product-facing teams.
You might thrive in this role if you:
Have strong hands-on experience training, evaluating, or debugging large ML models, especially LLMs.
Have excellent engineering skills in Python and modern ML frameworks such as PyTorch.
Bring mathematical rigor, quantitative taste, and comfort turning ambiguous research questions into measurable experiments.
Have experience with reinforcement learning, post-training, preference optimization, scalable oversight, model evaluation, or adjacent empirical ML research.
Can operate with high independence and do not need close day-to-day handholding.
Enjoy fast-paced, collaborative research environments where priorities shift as models and evidence change.
Have a strong record in technical problem solving, such as competitive programming, math contests, systems work, or similarly rigorous engineering and research projects.
Care about building AI systems that are trustworthy, honest, and reliable in high-stakes settings.
Are motivated by making concrete progress on alignment methods that can be tested, trained, published, and deployed.
About OpenAI
OpenAI 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.
For additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement.
Background checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.
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We are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link.
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At OpenAI, we believe artificial intelligence has the potential to help people solve immense global challenges, and we want the upside of AI to be widely shared. Join us in shaping the future of technology.
