研究科学家 - 多模态代理，消费设备
Research Scientist - Multimodal Agent, Consumer Devices
关于团队
计算的未来研究团队是消费者设备部门内的一个应用研究团队，专注于开发新的方法、模型和评估框架，以支持我们对计算未来愿景的支持。我们在多模态AI的前沿工作，帮助将新兴模型能力转化为有用、愉悦且值得长期信任的产品体验。
我们的工作探索一类新的AI系统，这些系统可以随时间学习、适应个人，并在日常生活的流程中提供支持。这包括长期记忆、用户建模和个人化系统，这些系统不仅与即时满足一致，还与个人的更广泛目标、价值观和福祉保持一致。
我们与研究、工程、设计、产品和安全团队紧密合作，定义构建能够随着时间了解你的AI系统的含义，能够在正确时刻行动，并以上下文感知、尊重和可证明有益的方式提供帮助。
关于职位
我们正在寻找一名研究工程师/科学家加入计算的未来研究团队，从事RLHF和个性化多模态AI系统的训练后工作。
该职位将专注于构建学习和评估基础，帮助模型随着时间变得更加上下文感知、适应性和实用性。您将参与解决奖励建模、偏好学习、长周期评估和策略改进等问题，这些问题是系统必须在现实用户环境中做出高质量行为决策时需要面对的。这项工作与产品紧密结合：成功不仅仅是更高的基准性能，而是在实际使用中更好的模型行为。
理想的候选人对超越单次助手行为、向通过反馈改进、从更丰富的信号中学习，并针对有意义的用户价值进行训练的系统充满热情。在内部，这与对精心设计的奖励、反馈循环和评估框架的需求密切相关，这些框架测试干预措施是否在更长的时间范围内确实有益。
该职位位于加利福尼亚州旧金山。我们采用每周3天在办公室的混合工作模式，并为新员工提供搬迁协助。
在此职位中，您将：
- 为多模态模型开发RLHF和训练后方法。
- 构建奖励模型和偏好学习管道，用于自适应、个性化的模型行为。
- 设计数据集、评分标准和评估框架，以捕捉用户优先事项
查看英文原文
About the Team
The Future of Computing Research team is an applied research team within the Consumer Devices group focused on developing new methods, models, and evaluation frameworks that support our vision for the future of computing. We work at the frontier of multimodal AI, helping turn emerging model capabilities into product experiences that are useful, delightful, and worthy of long-term trust.
Our work explores a new class of AI systems that can learn over time, adapt to individuals, and support people in the flow of daily life. This includes long-term memory, user modeling, and personalization systems that are aligned not just with immediate satisfaction, but with a person’s broader goals, values, and well-being.
We work closely across research, engineering, design, product, and safety to define what it means to build AI systems that know you over time, act at the right moment, and help in ways that are context-aware, respectful, and demonstrably beneficial.
About the Role
We are looking for a Research Engineer / Scientist to join the Future of Computing Research team to work on RLHF and post-training for personalized, multimodal AI systems.
This role will focus on building the learning and evaluation foundations that help models become more context-aware, adaptive, and useful over time. You will work on problems such as reward modeling, preference learning, long-horizon evaluation, and policy improvement for systems that must make high-quality behavioral decisions in realistic user settings. The work is deeply product-grounded: success is not just higher benchmark performance, but better model behavior in real-world use.
The ideal candidate is excited about pushing beyond one-turn assistant behavior toward systems that improve through feedback, learn from richer signals, and are trained against meaningful notions of user value. Internally, that maps closely to the need for careful reward design, feedback loops, and evaluation frameworks that test whether interventions are actually beneficial over longer horizons.
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:
- Develop RLHF and post-training methods for multimodal models.
- Build reward models and preference-learning pipelines for adaptive, personalized model behavior.
- Design datasets, rubrics, and evaluation frameworks that capture user preferences, contextual appropriateness, and long-term value in realistic tasks.
- Run experiments on policy improvement using explicit feedback, implicit signals, and model-based grading.
- Work on long-horizon evaluation problems, where model quality depends not just on a single response but on whether behavior improves outcomes over time.
- Collaborate closely with safety researchers to ensure that adaptation and personalization remain aligned, interpretable, and bounded by clear constraints.
- Prototype and iterate quickly on training recipes, reward formulations, data pipelines, and evaluation suites for product-relevant behaviors.
- Help define how OpenAI measures success for personalized AI systems including trust, appropriateness, and long-term user benefit.
You might thrive in this role if you:
- Have a strong background in machine learning research, with experience in RLHF, reward modeling, preference optimization, or post-training for large models.
- Have worked on one or more of: reinforcement learning, ranking, recommender systems, personalization, memory, or human-in-the-loop evaluation.
- Care about rigorous empirical work and know how to design clean experiments, reliable evals, and decision-useful metrics.
- Are excited by the challenge of training models against nuanced behavioral objectives.
- Have experience building datasets or eval pipelines grounded in human preferences, rubrics, or real-world product behavior.
- Are comfortable working across the stack, from data generation and labeling strategy to training runs, reward functions, and analysis.
- Are interested in multimodal AI and in how models can learn from richer interaction signals over time.
- Want to work on product-shaping research with unusually high stakes for trust, alignment, and long-term user value.
- Enjoy close collaboration with engineers, designers, and safety researchers to turn frontier research into real systems.
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 https://cdn.openai.com/policies/eeo-policy-statement.pdf.
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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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.