Research Engineer/Research Scientist, RL/Reasoning
About the Team The RL and Reasoning team drives the core reasoning paradigm and has created groundbreaking innovations such as o1 and o3. They focus on pushing the boundaries of reinforcement learning research, building next-generation generative models, and deploying them at scale.
What this role actually needs.
Research Engineer/Research Scientist, RL/Reasoning at OpenAI in San Francisco. UpJobz keeps this listing high-signal for applicants targeting serious high-tech roles across the United States, Canada, and Mexico. About the Team The RL and Reasoning team drives the core reasoning paradigm and has created groundbreaking innovations such as o1 and o3. They focus on pushing the boundaries of reinforcement learning research, building next-generation generative models, and deploying them at scale.
Day-to-day expectations
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- You love being on the cutting edge of RL and language model research.
- You’re a self-starter who takes initiative and ownership of ideas, driving them to completion.
- You value principled approaches, simple experiments in tightly-controlled settings, and reaching trustworthy conclusions which stand the test of time.
- You thrive in a fast-paced, dynamic, and technically complex environment where rapid iteration is key.
- You’re comfortable diving into a large ML codebase to debug and improve it.
- You have a deep understanding of machine learning and machine learning applications.
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- Tailor your resume around ai and llm instead of sending a generic application.
- Use the first two bullets of your application to connect your background directly to research engineer/research scientist, rl/reasoning is a high-signal on-site role in san francisco, and it is most realistic for united states residents.
- Open the role quickly if it fits and bookmark three similar jobs before you leave the page.
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- $295K - $445K is visible, so calibrate your application around the posted range.
- Use united states residents as part of your positioning so the recruiter does not have to infer it.
- Show concrete examples of succeeding in on-site environments.
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