Principal Research Engineer, Post-Training

The role

You will drive the technical vision and execution of systems that transform foundation models into intelligent products. You will lead initiatives in data, algorithms, infrastructure, and evaluation to improve model performance and user experience.

What you will do

• Define and drive the technical roadmap for mid- and post-training systems.
• Mentor and grow a team of researchers and engineers.
• Establish best practices for experimentation, model development, and deployment.
• Lead the development of alignment algorithms, optimization techniques, and training objectives.
• Drive advances in mid- and post-training methodologies.
• Identify and execute high-impact research opportunities.
• Develop robust evaluation frameworks and quality signals.
• Lead the design of efficient training and inference systems for large-scale generative models.
• Architect scalable data pipelines.
• Partner with infrastructure teams to optimize distributed training, GPU utilization, and serving efficiency.

Required qualifications

• PhD in Computer Science, Machine Learning, AI, or a related field, or equivalent industry experience.
• Significant experience leading technical projects or teams in machine learning, AI research, or large-scale distributed systems.
• Experience scaling and mentoring high-performing research and engineering teams.
• Deep understanding of modern machine learning techniques, including transformers, reinforcement learning, alignment methods, and large language models.
• Strong track record of delivering impactful research or applied ML systems in production environments.
• Expertise in designing, building, and maintaining production-quality ML systems and infrastructure.
• Experience training, serving, debugging, and optimizing large-scale models on GPU-based systems.
• Experience leading teams working on large language model training, mid-training, or post-training.
• Experience with product experimentation, online evaluation, and A/B testing frameworks.
• Strong software engineering skills with the ability to write clean, maintainable, and scalable code.
• Excellent communication skills and the ability to influence technical direction across teams.

Nice to have

• Hands-on experience working directly with open-source models like Mistral and Qwen, particularly adapting them via mid- and post-training for specific personas, creative writing, or role-playing appli
• Familiarity with cloud-native ML infrastructure, including Kubernetes, Docker, and modern orchestration platforms.
• Publications in leading machine learning conferences or demonstrated contributions to the broader AI community.

About the company

Character.AI empowers people to connect, learn and tell stories through interactive entertainment. The platform lets users engage with tens of millions of characters and embark on infinite adventures.