Research Engineer, Post-training Instruction Following
2 months ago
About the Team
Our post-training team are the chefs behind GPT-4 and o1-preview, cooking up the raw ingredients of base models into something nutritious, tasty, and non-toxic for consumers.
If you care about impact, this could be a good team for you. Your daily work will involve pushing the leading edge of technology and make a real difference to hundreds of millions of people across thousands of products.
About the Role
We are seeking a research engineer to help us post-train some of the world’s most powerful, cutting-edge AI models, used by hundreds of millions of people.
In particular, we’re looking for an early, impactful hire on a subteam focused on training models to more reliably do what’s asked of them. Lots of low hanging fruit to be picked, so lots of room for impact and growth.
This role is in San Francisco, CA. We nominally expect at least 3 days in the office per week, not because we care about where you sit, but because we care about the value you produce and believe that you’ll be best positioned to learn, teach, and succeed when sitting alongside collaborators. If you don’t already live here, we’ll assist you with relocation.
In this role, you will:
Train state-of-the-art language models using new techniques and new data
Become fluent in OpenAI’s deep learning infrastructure
Create evaluations to measure success
Rapidly iterate through experiments to find what works and what doesn’t
Prioritize approaches that (a) scale with compute and (b) endure as capabilities rise
Collaborate with product teams to ensure your work actually translates to better experiences for people using GPT
You might thrive in this role if you:
The only truly required qualification is that you’re able to learn to do the job and adapt as it changes. However, we’ll have more confidence in hiring you if you demonstrate a decent fraction of the following:
Strong software engineering skills (e.g., good at the command line, good at shaping the right abstractions, good at debugging, good at anticipating future design needs)
Strong Python skills (able to write high-quality readable code, and read others’ code)
Experience wrangling distributed systems
Experience managing projects in complex technical environments
Good intuitions of fundamental ML concepts (e.g., fluent in thinking about overfitting, generalization, reward hacking, etc.)
Good intuitions of language models and their quirks (e.g., why is it hard to count the R’s in strawberry, why chain of thought works)
Eagerness to dig into data and play with trained models
Curiosity about how to push the frontiers of AI performance
[Bonus] Experience fine-tuning large language models
[Bonus] Experience deploying large language models in a product, or using the OpenAI API
[Bonus] Building front end interfaces for looking at data, sharing results, etc.
This might be a bad role for you if:
You want to work deeply on a single problem for a long time
You want to publish your findings
You want to write elegant code without interacting with downstream users
You want to set new records on academic benchmarks
You’re more interested in model architecture than training / evaluation / data
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