Technical Lead, Safety Research
OpenAISafety SystemsPosted 23 February 2026
Job Description
About the TeamThe Safety Systems team is responsible for various safety work to ensure our best models can be safely deployed to the real world to benefit the society and is at the forefront of OpenAI's mission to build and deploy safe AGI, driving our commitment to AI safety and fostering a culture of trust and transparency.The Safety Research team aims to fundamentally advance our capabilities for precisely implementing robust, safe behavior in AI models and systems. As capabilities continue to advance, it is imperative that our approaches to safety continue to improve and scale to address evolving risks. This is important both for ensuring our systems are robust to prevent harmful misuse as well as ensuring potential misalignment cannot cause harm. We are working on these problems in a way that is grounded in our current models and methods but that generalizes to future systems.We are growing our team to expand our research on methods that will improve safety for AGI and beyond. This will include exploratory research for example, new methods to improve safety common sense and generalizable reasoning, developing new evaluations to elicit or detect misalignment or inner goals of the AI, and new methods to support human oversight of long-running tasks.About the RoleAs a tech lead, you will be responsible for developing our strategy in new directions to address potential harms from misalignment or significant mistakes. This will in practice include:Setting north star goals and milestones for new research directions, and developing challenging evaluations to track progress.Personally driving or leading research in new exploratory directions to demonstrate feasibility and scalability of the approaches.Working horizontally across safety research and related teams to ensure different technical approaches work together to achieve strong safety results.We’re looking for people who have a strong track record of practical research on safety and alignment, ideally in AI and LLMs, and have led large research efforts in the past.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:Set the research directions and strategies to make our AI systems safer, more aligned and more robust.Coordinate and collaborate with cross-functional teams, including the rest of the research organization, T&S, policy and related alignment teams, to ensure that our AI meets the highest safety standards.Actively evaluate and understand the safety of our models and systems, identifying areas of risk and proposing mitigation strategies.Conduct state-of-the-art research on AI safety topics such as RLHF, adversarial training, robustness, and more.Implement new methods in OpenAI’s core model training and launch safety improvements in OpenAI’s products.You might thrive in this role if you:Are excited about OpenAI’s mission of building safe, universally beneficial AGI and are aligned with OpenAI’s charterDemonstrate a passion for AI safety and making cutting-edge AI models safer for real-world use.Bring 4+ years of experience in the field of AI safety, especially in areas like RLHF, adversarial training, robustness, fairness & biases.Hold a Ph.D. or other degree in computer science, machine learning, or a related field.Possess experience in safety work for AI model deploymentHave an in-depth understanding of deep learning research and/or strong engineering skills.Are a team player who enjoys collaborative work environments.About OpenAIOpenAI 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 ... (truncated, view full listing at source)
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