Research Engineer, Post-Training (All Industry Levels)

About the role and team

Joining us as a Research Engineer on the Post-Training team, you'll be diving into the exciting world of fine-tuning AI models, optimizing their performance, and ensuring they meet the highest standards of quality and efficiency. Your work will directly contribute to our groundbreaking advancements in AI, helping shape an era where technology is not just a tool, but a companion in our daily lives. At Character.AI, your talent, creativity, and expertise will not just be valued—they will be the catalyst for change in an AI-driven future.

The Post-Training team is responsible for developing our powerful pretrained language models into intelligent, engaging, and aligned products.

As a Post-Training Researcher, you will work across teams and our technical stack to improve our model performance and training methods, including data, compute and algorithms. You will get to shape the conversational experience of millions of users per day.

What you'll do

• Develop alignment algorithms and loss functions to improve data sample efficiency.
• Write data pipelines to process diverse web data into a format models can ingest.
• Identify quality signals to understand our model’s performance in the real world.
• Design sampling algorithms to improve serving efficiency of large generative models.

Who you are

• "All Industry Levels": have at least PhD (or equivalent)
• 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 (reinforcement learning, transformers, etc)
• Track-record of exceptional research or creative applied ML projects

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)
• Publications in relevant academic journals or conferences in the field of machine learning