(PHD) Applied Machine Learning Engineer

NationGraph•San Francisco, CA

2d•Onsite

About The Position

Build and productionize end-to-end ML pipelines. Mine data from the web through large-scale crawling and scraping to power our models and insights. Transform unstructured text data into structured knowledge with NLP, entity recognition, and custom models. Build and improve text classification models to organize complex data. Optimize retrieval-augmented generation (RAG) systems used in our product. Drive our data strategy by identifying new data sources. Solve open-ended technical problems, teaching, learning, and iterating with the team. Work primarily in Python and SQL.

Requirements

A quantitative background (e.g., computer science, physics, math, or engineering)
A strong mathematical and statistical foundation
A doctorate degree in a quantitative field
Proficiency in Python
A strong sense of ownership and ability to work on open-ended technical problems to drive commercial impact
A passion for learning and growth, and for uncovering insights in complex data
Excellent problem-solving, communication, and collaboration skills in a fast-paced environment

Responsibilities

Build and productionize end-to-end ML pipelines.
Mine data from the web through large-scale crawling and scraping to power our models and insights.
Transform unstructured text data into structured knowledge with NLP, entity recognition, and custom models.
Build and improve text classification models to organize complex data.
Optimize retrieval-augmented generation (RAG) systems used in our product.
Drive our data strategy by identifying new data sources.
Solve open-ended technical problems, teaching, learning, and iterating with the team.