- 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, ...).
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Research Engineer - Menlo Park, United States - Character
Description
About the roleWe'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:
Founded in 2021 by AI pioneers Noam Shazeer and Daniel De Freitas, Character is a full-stack AI powerhouse and ranks among the most utilized AI research platforms globally. Our innovative approach allows users to customize their experience with personalized AI 'Characters.'
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.
Noam co-invented core LLM tech and was recently honored as one of TIME's 100 Most Influential in AI. Daniel created LaMDA, the breakthrough conversational AI now powering Google's Bard.
We encourage you to apply even if you don't meet all qualifications. Underrepresented individuals often experience imposter syndrome-don't underestimate yourself.
Our commitment to diversity:
Character values diversity and welcomes applicants of all backgrounds. We are an equal opportunity employer and 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 us.
