PhD or equivalent experience in Computer Science, Machine Learning, or related discipline
Ability to write clear and production-grade code
Experience with GPUs for training, serving, and debugging
Proficient in data pipelines and infrastructure
Strong understanding of modern machine learning techniques, especially transformers and reinforcement learning related to safety
Dedicated to responsible AI development and addressing complex safety challenges
Responsibilities
Develop and implement evaluation methods to assess AI safety and alignment
Research and innovate techniques for model alignment and interpretability
Conduct adversarial testing to identify vulnerabilities in models
Analyze and reduce biases and harmful behaviors using techniques like RLHF
Collaborate with engineering teams to implement safety findings into products
Contribute to AI safety knowledge through publications and presenting research
Benefits
Join a rapidly growing, innovative company recognized as Google Play's AI App of the Year
Work in a diverse and inclusive environment that values unique perspectives
Participate in cutting-edge AI research that impacts millions of users
Engage with a vibrant community of AI and technology enthusiasts
Access to learning and professional development opportunities
Full Job Description
About the role and team
Joining us as a Research Engineer, you'll be at the forefront of tackling one of the most critical challenges in AI today: safety and alignment. Your work will be pivotal in understanding and mitigating the risks of advanced AI, conducting foundational research to make our models safer, and solving the core technical problems of AI alignment-ensuring our models behave in accordance with human values and intentions.
The Safety team is dedicated to pioneering and implementing techniques that make our models more robust, honest, and harmless. As a Research Engineer, you will bridge the gap between theoretical research and practical application, writing high-quality code to test hypotheses and integrating successful safety solutions directly into our products. Your research will not only protect millions of users but also contribute to the broader scientific community's understanding of how to build safe, beneficial AI.
What you'll do
Develop and implement novel evaluation methodologies and metrics to assess the safety and alignment of large language models.
Research and develop cutting-edge techniques for model alignment, value learning, and interpretability.
Conduct adversarial testing to proactively uncover potential vulnerabilities and failure modes in our models.
Analyze and mitigate biases, toxicity, and other harmful behaviors in large language models through techniques like reinforcement learning from human feedback (RLHF) and fine-tuning.
Collaborate with engineering and product teams to translate safety research into practical, scalable solutions and best practices.
Stay abreast of the latest advancements in AI safety research and contribute to the academic community through publications and presentations.
Who you are
Hold a PhD (or equivalent experience) in a relevant field such as Computer Science, Machine Learning, or a related discipline.
Write clear and clean production-facing and training code
Experience working with GPUs (training, serving, debugging)
Experience with data pipelines and data infrastructure
Strong understanding of modern machine learning techniques, particularly transformers and reinforcement learning, with a focus on their safety implications.
Are passionate about the responsible development of AI and dedicated to solving complex safety challenges.
Nice to Have
Experience with product experimentation and A/B testing
Experience training large models in a distributed setting
Familiarity with ML deployment and orchestration (Kubernetes, Docker, cloud)
Experience with explainable AI (XAI) and interpretability techniques.
Have research in AI safety, alignment, ethics, or a related area.
Knowledge of the broader societal and ethical implications of AI, including policy and governance.
Publications in relevant academic journals or conferences in the field of machine learning
