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.
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.
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.
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
Character.AI empowers people to connect, learn and tell stories through interactive entertainment. Over 20 million people visit Character.AI every month, using our technology to supercharge their creativity and imagination. Our platform lets users engage with tens of millions of characters, enjoy unlimited conversations, and embark on infinite adventures.
In just two years, we achieved unicorn status and were honored as Google Play's AI App of the Year—a testament to our innovative technology and visionary approach.
Join us and be a part of establishing this new entertainment paradigm while shaping the future of Consumer AI!
At Character, we value diversity and welcome applicants from all backgrounds. As an equal opportunity employer, we firmly uphold a non-discrimination policy based on race, religion, national origin, gender, sexual orientation, age, veteran status, or disability. Your unique perspectives are vital to our success.
Own post-training research for legal AI models, developing SFT and reinforcement learning methods, reward systems, and evaluations to improve long-horizon reasoning and agentic workflows.
Optimize multimodal models for efficient training and deployment by profiling hardware performance, developing high-performance GPU kernels, and scaling transformer implementations across distributed systems.
Research and exploit vulnerabilities in LLM applications and autonomous agents, building attack tools, agent harnesses, and reproducible evaluations that improve AI security and offensive security products.
Design and deliver production-grade optimization models and solver services for enterprise contact center workforce planning, scheduling, staffing, coverage, and resource allocation.
Design and deliver production-grade optimization models and solver services for workforce planning, scheduling, staffing, and resource allocation in a large-scale contact center environment.
