Member of Technical Staff, Data Engineering

ABOUT MITHRL

We envision a world where novel drugs and therapies reach patients in months, not years, accelerating breakthroughs that save lives.

Mithrl is building the world’s first commercially available AI Co-Scientist—a discovery engine that empowers life science teams to go from messy biological data to novel insights in minutes. Scientists ask questions in natural language, and Mithrl answers with real analysis, novel targets, and patent-ready reports.

Our traction speaks for itself:

• 12X year-over-year revenue growth

• Trusted by leading biotechs and big pharma across three continents

• Driving real breakthroughs from target discovery to patient outcomes.

WHAT YOU WILL DO

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.

WHAT YOU BRING

Must-have

• 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.