About the role
As a Member of Technical Staff - Engineering (Data Infrastructure), you will own the systems that turn large, real-world datasets into data Collinear can build on. Our environments are grounded in terabytes of data, spread across archives, spreadsheets, email, PDFs, and scanned documents. The pace at which we process these determines how fast we deliver to frontier labs.
This is a hands-on role at the intersection of algorithms, distributed systems, and data quality. Many of our hardest problems, such as linking related records across millions of files, don't split up neatly, and you will define how we solve them at scale.
What you'll do
Build pipelines that process multi-terabyte datasets in parallel across archives, spreadsheets, email, PDFs, and scanned documents
Design graph-based systems that link related records, such as the same person or company appearing across millions of files
Build fast string and pattern search over large, heterogeneous datasets
Profile and remove bottlenecks, and decide how to split work that doesn't parallelize neatly
Transform data for downstream use, including consistently replacing sensitive fields across files
Define how we measure data quality, and build review tools so the team can catch and fix errors without reprocessing everything
Assess new datasets and filter out low-quality data before it reaches our environments
About you
You have 5+ years of experience building data-intensive systems in production
You have processed large datasets in parallel with frameworks such as Apache Spark, Ray, or Dask, and know when to design your own
You have a strong command of graph algorithms, and experience using them to transform large amounts of data
You have built efficient string matching and pattern search at scale, such as fuzzy matching or indexing
You make sound tradeoffs between accuracy, speed, and cost, and can explain them clearly
Nice to have
Experience with entity resolution or record linkage
Experience with NLP or LLM-based information extraction
OCR or document processing experience, including poor scans and handwriting
Experience in a systems language such as Rust, C++, or Go
Experience with regulated or sensitive data, such as financial or healthcare records