Researcher designing and running experiments on chain-of-thought monitorability in frontier LLMs to support scalable oversight and alignment. Requires strong empirical ML expertise with LLMs, deep interest in model behavior/alignment/interpretability, and ability to translate ambiguous questions into concrete experiments.
Researcher building frontier RL environments, evaluations, and training signals to steer OpenAI's largest agent training runs and measure model capabilities.
This role focuses on advancing how OpenAI builds and understands pretraining data at scale. The individual will treat data quality and curation as core research problems, developing new methods to select, combine, and transform data to improve model capabilities.
OpenAI is seeking a Research Engineer/Research Scientist to advance how the company prepares, curates, synthesizes, and understands multimodal data at scale. This role involves working on research and production problems related to synthesizing multimodal content, improving data pipelines, and building quality filters.
Senior researcher studies how training choices shape aligned behavior in frontier models, developing synthetic data, evaluation loops, and experiments to ensure durable, robust tendencies like honest reasoning and instruction-following.
Designs worst-case demonstrations and adversarial evaluations to uncover AGI misalignment risks like deception and power-seeking. Builds automated stress-testing infrastructure and researches alignment failure modes to inform OpenAI's safety strategy. Requires 4+ years in AI red-teaming or adversarial ML.
Designs and runs experiments to improve AI model intent alignment, honesty, calibration, and robustness using RL and empirical ML methods. Trains/evaluates large models like LLMs and integrates techniques into production workflows.
Develops RLHF and post-training methods for personalized, multimodal AI systems on consumer devices, focusing on reward modeling, preference learning, long-horizon evaluation, and alignment with user values. Requires strong ML research background in RLHF and related areas.
Designs and implements mitigation stacks to prevent loss of control risks in frontier AI models, including prevention, monitoring, detection, and enforcement. Requires expertise in deep learning, transformers, PyTorch/TensorFlow, and AI safety research.
Develops novel reinforcement learning techniques using synthetic environments and feedback to enhance large-scale AI models. Designs experiments, analyzes dynamics, and integrates research into production systems; requires strong RL/ML background and engineering skills.
Research and develop improvements to pre-trained models for deployment in ChatGPT and API using reinforcement learning and product-driven approaches. Requires strong ML engineering, research experience with novel models, and ability to debug large codebases.
Develop techniques to predict and mitigate unsafe behaviors in early-stage base models, design safer pretraining architectures, and integrate safety signals throughout training. Collaborate across safety teams to build robust, scalable safety foundations grounded in real-world risks.
Advances AI coding models through research, experimentation, and system optimization on the Codex team. Collaborates to improve code generation, reasoning, and performance for real-world deployment.
Develops embedding models and retrieval systems to enable frontier AI models to access relevant information dynamically. Requires deep expertise in representation learning, vector retrieval, and transformer LLMs, with experience scaling ML systems.
Conducts research in mechanistic interpretability to understand deep network representations and enhance AI safety. Requires PhD or equivalent in ML/CS, 2+ years research engineering with Python, and passion for safe AGI.
Advances AI capabilities through cutting-edge reinforcement learning research, training intelligent agents for models like o1 and o3. Requires RL research background, strong coding skills, and ability to iterate quickly in a fast-paced environment.
Builds ambitious RL environments and evaluation systems to measure and steer frontier AI models toward safe AGI. Requires strong ML research engineering, statistical skills, and red-teaming mindset for end-to-end project ownership in fast-paced setting.
Builds advanced AI systems using massive-scale distributed machine learning to achieve breakthrough performance. Requires strong programming skills and experience with large distributed systems.
Develops safe and reliable AI models for healthcare applications using techniques like RLHF, automated red teaming, and scalable oversight. Requires PhD, 4+ years in deep learning/LLM research, and focus on practical AI safety improvements.
Conducts research on societal impacts of AI models, translates policy problems into technical evaluations, and develops interventions for safe AGI deployment. Requires 3+ years research experience, Python proficiency, and expertise in AI safety techniques like RLHF.
Designs and implements experiments for AI alignment research, focusing on scalable solutions for human intent following, risk evaluation, model robustness, and oversight methods in complex scenarios. Requires PhD-level research experience and strong ML engineering skills.
Senior researcher leading AI safety initiatives, conducting cutting-edge research on RLHF, adversarial training, and robustness to make AI systems safer and more aligned. Requires 4+ years in AI safety, PhD in ML/CS, and deep learning expertise.
Researcher designing and running experiments on chain-of-thought monitorability in frontier LLMs to support scalable oversight and alignment. Requires strong empirical ML expertise with LLMs, deep interest in model behavior/alignment/interpretability, and ability to translate ambiguous questions into concrete experiments.
Researcher building frontier RL environments, evaluations, and training signals to steer OpenAI's largest agent training runs and measure model capabilities.
This role focuses on advancing how OpenAI builds and understands pretraining data at scale. The individual will treat data quality and curation as core research problems, developing new methods to select, combine, and transform data to improve model capabilities.
OpenAI is seeking a Research Engineer/Research Scientist to advance how the company prepares, curates, synthesizes, and understands multimodal data at scale. This role involves working on research and production problems related to synthesizing multimodal content, improving data pipelines, and building quality filters.
Senior researcher studies how training choices shape aligned behavior in frontier models, developing synthetic data, evaluation loops, and experiments to ensure durable, robust tendencies like honest reasoning and instruction-following.
Designs worst-case demonstrations and adversarial evaluations to uncover AGI misalignment risks like deception and power-seeking. Builds automated stress-testing infrastructure and researches alignment failure modes to inform OpenAI's safety strategy. Requires 4+ years in AI red-teaming or adversarial ML.
Designs and runs experiments to improve AI model intent alignment, honesty, calibration, and robustness using RL and empirical ML methods. Trains/evaluates large models like LLMs and integrates techniques into production workflows.
Develops RLHF and post-training methods for personalized, multimodal AI systems on consumer devices, focusing on reward modeling, preference learning, long-horizon evaluation, and alignment with user values. Requires strong ML research background in RLHF and related areas.
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Designs and implements mitigation stacks to prevent loss of control risks in frontier AI models, including prevention, monitoring, detection, and enforcement. Requires expertise in deep learning, transformers, PyTorch/TensorFlow, and AI safety research.
Develops novel reinforcement learning techniques using synthetic environments and feedback to enhance large-scale AI models. Designs experiments, analyzes dynamics, and integrates research into production systems; requires strong RL/ML background and engineering skills.
Research and develop improvements to pre-trained models for deployment in ChatGPT and API using reinforcement learning and product-driven approaches. Requires strong ML engineering, research experience with novel models, and ability to debug large codebases.
Develop techniques to predict and mitigate unsafe behaviors in early-stage base models, design safer pretraining architectures, and integrate safety signals throughout training. Collaborate across safety teams to build robust, scalable safety foundations grounded in real-world risks.
Advances AI coding models through research, experimentation, and system optimization on the Codex team. Collaborates to improve code generation, reasoning, and performance for real-world deployment.
Develops embedding models and retrieval systems to enable frontier AI models to access relevant information dynamically. Requires deep expertise in representation learning, vector retrieval, and transformer LLMs, with experience scaling ML systems.
Conducts research in mechanistic interpretability to understand deep network representations and enhance AI safety. Requires PhD or equivalent in ML/CS, 2+ years research engineering with Python, and passion for safe AGI.
Advances AI capabilities through cutting-edge reinforcement learning research, training intelligent agents for models like o1 and o3. Requires RL research background, strong coding skills, and ability to iterate quickly in a fast-paced environment.
Builds ambitious RL environments and evaluation systems to measure and steer frontier AI models toward safe AGI. Requires strong ML research engineering, statistical skills, and red-teaming mindset for end-to-end project ownership in fast-paced setting.
Builds advanced AI systems using massive-scale distributed machine learning to achieve breakthrough performance. Requires strong programming skills and experience with large distributed systems.
Develops safe and reliable AI models for healthcare applications using techniques like RLHF, automated red teaming, and scalable oversight. Requires PhD, 4+ years in deep learning/LLM research, and focus on practical AI safety improvements.
Conducts research on societal impacts of AI models, translates policy problems into technical evaluations, and develops interventions for safe AGI deployment. Requires 3+ years research experience, Python proficiency, and expertise in AI safety techniques like RLHF.
Designs and implements experiments for AI alignment research, focusing on scalable solutions for human intent following, risk evaluation, model robustness, and oversight methods in complex scenarios. Requires PhD-level research experience and strong ML engineering skills.
Senior researcher leading AI safety initiatives, conducting cutting-edge research on RLHF, adversarial training, and robustness to make AI systems safer and more aligned. Requires 4+ years in AI safety, PhD in ML/CS, and deep learning expertise.