Machine Learning Research Engineer (NLP)

Key Skills

Java Programming, Python Programming, C++, C Programming, SQL, Cloud computing, Scala Programming, Machine learning techniques, Data science techniques, MATLAB Programming, PyTorch, TensorFlow, R Programming

Job Description

Ought is a product-driven machine learning lab building Elicit, an AI research assistant. Elicit uses language models to automate and support research processes like literature and evidence review. Elicit applies frontier technology to serious use cases, enabling our research team to understand in great detail where language models fail and how to mitigate such failures.

For more roles and to learn more about Ought, see the main careers page.

About the role

As an ML research engineer at Ought, you will:

• Compose together multiple calls to language models to accomplish tasks that we can't accomplish with a single call. A good way to learn what this is like and demonstrate how you'd think about this is to go through our Factored Cognition Primer and submit solutions for some of the exercises
• Curate datasets for finetuning models, e.g. for training models to extract policy conclusions from papers
• Set up evaluation metrics that tell us what changes to our models or training setup are improvements
• Scale up semantic search from a few thousand documents to 100k+ documents

About you

To help us get there, you'll need:

• A strong software engineering background. We want to apply your experience building systems, designing architecture, and thinking about good abstractions. Ought will need you to do much more than write scripts.
• Familiarity with language models (training, finetuning, evaluation), or comparable machine learning or natural language processing background (e.g. experience with information extraction, semantic search)
• A startup mindset. We expect to measure our impact in part by the people whose lives we improve through reasoning and models of the future. We know you care about that too. You’ll want to test lots of ideas, get feedback, and watch yourself learning and growing every day.