Research Software Engineer, Data Quality (Post-training)

About the Team

Our team is responsible for training the ChatGPT models used by millions of users in both ChatGPT and the API.

About the Role

One of the most important parts of improving ChatGPT is building and training on high quality datasets. We are looking for engineers to help us build data pipelines and conduct research explorations related to data quality.

Ideal candidates should have a strong technical background and general knowledge. Given how coupled our data systems are with the underlying large language models, candidates should have familiarity with the quickly emerging LLM stack and experience in an applied Machine Learning setting.

This role is based in San Francisco, CA. We use a hybrid work model of 3 days in the office per week and offer relocation assistance to new employees.

In this role, you will:

• Build systems and pipelines for continually processing and filtering large amounts of data.
• Train and run Machine Learning classifiers and embeddings on large amounts of data to sort and categorize by data quality and other attributes.
• Work with researchers on dataset creation and dataset prep to kick off experiments.
• Lead other research bets involving data and data quality.

You might thrive in this role if you:

• Have 3+ years of experience working with data-intensive applications or distributed systems and 6+ years of any software engineering experience (including data engineering).
• Highly skilled with Python.
• Ability to write, debug and optimize Spark code, and understanding of data orchestration and automation tools (e.g. Airflow).
• Familiarity with Machine Learning, Deep Learning, and Large Language Models and related infrastructure (e.g. PyTorch)
• Bonus: Experience working with embeddings and vector libraries.
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