Research Engineer, Post-Training

5 Months ago • All levels • Artificial Intelligence • Undisclosed

About the job

Job Description

Develop alignment algorithms, write data pipelines, understand model performance, and design sampling algorithms for large generative models.
Must have:
  • Production-facing code
  • GPU Experience
  • Data Pipelines
  • ML Techniques
Good to have:
  • A/B testing
  • Large Model Training
  • ML Deployment
  • Academic Publications
Perks:
  • Shape the future
  • Diverse Environment

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.

About the role

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.

Example projects:

  • 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.

Job Requirements

  • 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

About Character.AI

Founded in 2021, Character is a leading AI company offering personalized experiences through customizable AI 'Characters.' As one of the most widely used AI platforms worldwide, Character enables users to interact with AI tailored to their unique needs and preferences.

In just two years, we achieved unicorn status and were named Google Play's AI App of the Year – a testament to our groundbreaking technology and vision.

Ready to shape 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.

Compensation Range: $150K - $350K

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About The Company

Character is one of the world's leading personal AI platforms. Founded in 2021 by AI pioneers Noam Shazeer and Daniel De Freitas, Character is a full-stack AI company with a globally scaled direct-to-consumer platform. 

Menlo Park, California, United States (On-Site)

Menlo Park, California, United States (On-Site)

Menlo Park, California, United States (On-Site)

Menlo Park, California, United States (On-Site)

Menlo Park, California, United States (On-Site)

Menlo Park, California, United States (On-Site)

Menlo Park, California, United States (On-Site)

Menlo Park, California, United States (On-Site)

New York, New York, United States (On-Site)

New York, New York, United States (On-Site)

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