Why This Job is Featured on The SaaS Jobs
This Researcher role sits at an increasingly important intersection for SaaS: shipping AI capabilities while maintaining control, accountability, and measurable reliability. As more SaaS products embed agentic models into real user workflows, oversight becomes part of the product surface, not just a research concern. The listing signals work that connects model behavior to operational safeguards, with an emphasis on techniques that can run on live traffic and influence how AI systems are deployed.
For a long-term SaaS career, the distinctive value is experience translating uncertain research ideas into repeatable systems that affect production outcomes. The work spans evaluation design, data-driven diagnosis of failures, and infrastructure that supports iteration, which mirrors how modern SaaS teams improve products through instrumentation and feedback loops. Exposure to human-in-the-loop controls and red-teaming also builds a practical understanding of risk management that is increasingly relevant across AI-enabled SaaS categories.
The role best fits professionals who enjoy moving between experimentation and implementation, and who are comfortable scoping work across multi-month research timelines while staying grounded in deployability. It will resonate with those drawn to cross-functional problem solving where research, engineering, and product constraints meet, particularly in an on-site environment tied to real deployment cycles.
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 Oversight team at OpenAI develops techniques for improving control, accountability, and alignment as AI systems become more capable and agentic.
We combine longer-horizon research with hands-on deployment. We study long-term questions about how increasingly intelligent systems can be supervised, constrained, and corrected, while also building oversight systems that are used in practice today, both internally and externally (see our recent work on code review and action monitoring for codex).
We also study how to learn from real-world deployments: using oversight data and human interventions to train future models to be more aligned, while preserving the effectiveness and independence of the oversight systems themselves.
About the Role
As a researcher on the Alignment team, you will design and run experiments that improve our ability to oversee increasingly capable models. You will work on hands-on model training, evaluation design, and research infrastructure, and translating promising oversight ideas into systems that can operate on real model traffic and real user workflows.
This role combines longer-horizon research with shorter deployment sprints, with projects typically scoped around 3-6 month research timelines and aimed at directly improving future model behavior.
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.
In this role, you will:
Design and implement alignment experiments focused on oversight systems for increasingly agentic AI models.
Deploy practical systems for action monitoring, red-teaming, and human-in-the-loop control.
Develop evaluations for alignment failure modes of the frontier models such as overeagerness, instruction following failures, covert actions, avoiding restrictions and scheming propensity.
Analyze deployment data to understand model failures, oversight gaps, and opportunities for training more aligned models.
Develop techniques for feeding oversight signals back into training while preserving the reliability and independence of the oversight process.
Produce externally publishable research when results advance the broader science of alignment.
Collaborate across research, product, security, safety, and engineering teams to turn alignment ideas into working systems.
Move quickly from research intuition to working experiments, prototypes, and evidence that can shape future models.
You might thrive in this role if you:
Have strong hands-on experience training, evaluating, or debugging large ML models, especially LLMs.
Have experience with reinforcement learning, post-training, preference optimization, scalable oversight, model evaluation, or adjacent empirical ML research.
Have strong engineering execution and can turn ambiguous research ideas into reliable experiments, tools, training pipelines, and production-facing systems.
Have research intuitions for what experiments are likely to teach us something, while staying grounded in implementation details and empirical results.
Are a team player - willing to do a variety of tasks that move the team forward.
Enjoy fast-paced, collaborative research environments where priorities shift as models and evidence change.
See safety and usefulness as coupled goals.
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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