Data Engineer - Scientific Data Ingestion (San Francisco)

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 worlds first commercially available AI Co-Scientista 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. No coding. No waiting. No bioinformatics bottlenecks.

We are the fastest growing tech-bio startup in the Bay Area with over 12X YoY revenue growth. Our platform is already being used by teams at some of the largest biotechs and big pharma across three continents to accelerate and uncover breakthroughsfrom target discovery to mechanism of action.

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.
• 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.

Nice-to-have

WHAT YOU WILL LOVE AT MITHRL