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- Determining the type of training data we need, finding where we can collect it, and writing distributed data gathering pipelines to ingest data
- Developing new model architectures that push the state-of-the-art in terms of quality, scale, and inference speed
- Creating new evaluations that capture different aspects of generative outputs
- Writing fast inference algorithms to serve these models at scale
- Working with product teams to integrate feedback mechanisms into the product, which we use to improve the model
- Need at least 2+ years of industry experience working deep in the weeds on hard ML problems.
- Negative example: just stringing together a bunch of pre-existing components together. Need signal that this person can think critically about different parts of the pipline
- Have a deep understanding of the "whole stack" when it comes to designing, training, evaluating and deploying machine learning models, especially large language models.
- Collected a new giant dataset
- Published research papers
- Played a critical role in shipping a new ML product that required custom components
- Writing distributed ML infrastructure
- Have debugged and fixed hard-to-find bugs in ML models
- Have a track record of successfully owning projects from start to finish.
- Have experience with generative models for various modalities.
- Experience working with proven tools: ML frameworks (Tensorflow, PyTorch, Jax, ...), data processing frameworks (Spark, Beam, ...).
Research Engineer - Menlo Park, United States - Character
Description
About usCharacter's mission is to empower everyone with AGI. Our vision is to enable people with our technology so that they can use Character.AI any moment of any day.
Character.AI is one of the world's leading personal AI platforms. Founded in 2021 by AI pioneers Noam Shazeer and Daniel De Freitas, Character.AI is a full-stack AI company with a globally scaled direct-to-consumer platform. As of 2023 that platform was #2 in the space in user engagement. Character.AI is uniquely centered around people, letting users personalize their experience by interacting with AI "Characters." The company achieved unicorn status in 2023 and was named Google Play's AI App of the Year.
Noam co-invented the key tech powering LLMs and was recently named to TIME100's Most Influential People in AI list. TIME called him "one of the most important and impactful people of the space's past, present, and future." Daniel created and led LaMDA, the breakthrough conversational tech project currently powering Bard.
To learn more, please visit
About the role
We're looking for scrappy and self-motivated people who have full-stack machine learning skills: collecting data, training state-of-the-art models, building evaluations, writing efficient inference algorithms, and iterating on user feedback.
In the day-to-day, you will be responsible for developing new AI capabilities end-to-end. This means you will need to wear a lot of hats across the full ML stack. You should be comfortable thinking about all parts of the problem, and ready to work on any and all components of it.
Responsibilities: