Research Engineer - Multimodal

4 Months ago • 2 Years + • Artificial Intelligence

About the job

Job Description

Research Engineer with 2+ years experience in ML, developing AI capabilities end-to-end, including data collection, model training, evaluation, inference, and user feedback.
Must have:
  • Full-stack ML skills
  • State-of-the-art models
  • Large language models
  • Distributed ML infrastructure
Good to have:
  • Generative models
  • TensorFlow, PyTorch, Jax
  • Spark, Beam
  • Product integration
Perks:
  • Shape the future
  • Unicorn status
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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:

  • 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

Requirements:

  • 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, …).

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)

New York, New York, 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)

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

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

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