Research Engineer/Scientist - Human Alignment, Consumer Devices
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
What this role actually needs.
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 Responsibilities: - 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. Company context: OpenAI builds frontier AI systems, research infrastructure, and applied products for developers, enterprises, and global users.
Day-to-day expectations
OpenAI lists these responsibilities for the Research Engineer/Scientist - Human Alignment, Consumer Devices role.
- 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.
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Source: jobs.ashbyhq.com · Source ID: 09253a0e-a2b8-49d1-80bb-6955ba3b08a3 · Confidence: 97/100 · Last checked: May 7, 2026
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