Member of Technical Staff, Mithrl

• 5+ years of experience in data engineering / data wrangling with real-world tabular or semi-structured data
• Strong fluency in Python, and data processing tools (Pandas, Polars, PyArrow, or similar)
• Excellent experience dealing with messy Excel / CSV / spreadsheet-style data — inconsistent headers, multiple sheets, mixed formats, free-text fields — and normalizing it into clean structures
• Comfort designing and maintaining robust ETL/ELT pipelines, ideally for scientific or lab-derived data
• Ability to combine classical data engineering with LLM-powered data normalization / metadata extraction / cleaning
• Strong desire and ability to own the ingestion & normalization layer end-to-end — from raw upload → final clean dataset — with an eye for maintainability, reproducibility, and scalability
• Good communication skills; able to collaborate across teams (product, bioinformatics, infra) and translate real-world messy data problems into robust engineering solutions
• We encourage you to apply even if you do not believe you meet every single qualification

Desirable

• Build and own an AI-powered ingestion & normalization pipeline to import data from a wide variety of sources — unprocessed Excel/CSV uploads, lab and instrument exports, as well as processed data from internal pipelines
• Develop robust schema mapping, coercion, and conversion logic (think: units normalization, metadata standardization, variable-name harmonization, vendor-instrument quirks, plate-reader formats, reference-genome or annotation updates, batch-effect correction, etc.)
• Use LLM-driven and classical data-engineering tools to structure “semi-structured” or messy tabular data — extracting metadata, inferring column roles/types, cleaning free-text headers, fixing inconsistencies, and preparing final clean datasets
• Ensure all transformations that should only happen once (normalization, coercion, batch-correction) execute during ingestion — so downstream analytics / the AI “Co-Scientist” always works with clean, canonical data
• Build validation, verification, and quality-control layers to catch ambiguous, inconsistent, or corrupt data before it enters the platform
• Collaborate with product teams, data science / bioinformatics colleagues, and infrastructure engineers to define and enforce data standards, and ensure pipeline outputs integrate cleanly into downstream analysis and storage systems