Why This Job is Featured on The SaaS Jobs
Work on model alignment sits at an increasingly important intersection of SaaS delivery and AI research, because many software products now embed foundation models directly into user workflows. This role is notable in that it targets the reliability layer of AI systems, focusing on intent-following, calibration, and robustness rather than feature development. The emphasis on translating research into deployed methods also reflects a SaaS reality: model behavior becomes a product surface area that must be measured, improved, and monitored over time.
For a long-term SaaS career, the value here is exposure to the full loop of experimentation, evaluation design, and operational integration. The work builds fluency in how model quality is defined and enforced, how failure modes are discovered and quantified, and how training and inference-time interventions can be paired with deployment constraints. Those are durable skills for teams building AI-enabled platforms, developer tools, or safety and governance layers around model-driven products.
The role best fits professionals who enjoy rigorous, quantitative work and can turn ambiguous questions into testable systems. It suits someone comfortable balancing research outputs with engineering deliverables, and who prefers work where success is demonstrated through evidence, reproducibility, and clear evaluation signals.
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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