WHAT YOU WILL DOBuild 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 BRINGMust-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
- Familiarity with scientific data types and "modalities" (e.g. plate-readers, genomics metadata, time-series, batch-info, instrumentation outputs).
- Experience with workflow orchestration tools (e.g. Nextflow, Prefect, Airflow, Dagster), or building pipeline abstractions.
- Experience with cloud infrastructure and data storage (AWS S3, data lakes/warehouses, database schemas) to support multi-tenant ingestion.
- Past exposure to LLM-based data transformation or cleansing agents - building or integrating tools that clean or structure messy data automatically.
- Any background in computational biology / lab-data / bioinformatics is a bonus - though not required.
WHAT YOU WILL LOVE AT MITHRL- Mission-driven impact: you'll be the gatekeeper of data quality - ensuring that all scientific data entering Mithrl becomes clean, consistent, and analysis-ready. You'll have outsized influence over the reliability and trustworthiness of our entire data + AI stack.
- High ownership & autonomy: this role is yours to shape. You decide how ingestion works, define the standards, build the pipelines. You'll work closely with our product, data science, and infrastructure teams - shaping how data is ingested, stored, and exposed to end users or AI agents.
- Team: Join a tight-knit, talent-dense team of engineers, scientists, and builders
- Culture: We value consistency, clarity, and hard work. We solve hard problems through focused daily execution
- Speed: We ship fast (2x/week) and improve continuously based on real user feedback
- Location: Beautiful SF office with a high-energy, in-person culture
- Benefits: Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top-tier plans
We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.
