(PHD) Applied Machine Learning Engineer

NationGraph•San Francisco, CA

4d•Onsite

About The Position

NationGraph is making public sector data accessible and actionable for businesses selling to cities, counties, state agencies, schools, and special districts. NationGraph’s data and intelligence engine provides buying signals derived from millions of public sector sources. Founded in 2024, NationGraph is dedicated to making uncommon knowledge common, because public data should actually be public. Learn more at nationgraph.com You’ll Join A Team That: Has successfully built, scaled, and sold companies in the past. Built software infrastructure processing billions of dollars in transactions. Is backed by world-class VCs and operating partners who’ve invested in, and built, iconic companies. About The Role: 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.
Work primarily in Python and SQL.