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Member of Technical Staff

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Member of Technical Staff in USA new

  • San Francisco, CA

Responsibilities

  • Build and own an AI-powered ingestion and normalization pipeline for Excel, CSV, laboratory, instrument, and internal pipeline data.
  • Develop schema mapping, coercion, unit normalization, metadata standardization, variable-name harmonization, batch correction, and vendor-format conversion logic.
  • Use LLM-driven and classical data-engineering tools to extract metadata, infer column roles and types, clean headers, resolve inconsistencies, and prepare canonical datasets.
  • Ensure one-time transformations occur during ingestion so downstream analytics and AI systems receive clean data.
  • Build validation, verification, and quality-control layers to identify ambiguous, inconsistent, or corrupt data.
  • Collaborate with product, data science, bioinformatics, and infrastructure teams to define data standards and integrate pipeline outputs with analytics and storage systems.

Requirements

  • 5+ years of experience in data engineering or data wrangling with real-world tabular or semi-structured data.
  • Strong Python fluency and experience with data-processing tools such as Pandas, Polars, or PyArrow.
  • Extensive experience handling and normalizing messy Excel, CSV, and spreadsheet-style data.
  • Experience designing and maintaining robust ETL/ELT pipelines, ideally for scientific or laboratory data.
  • Ability to combine classical data engineering with LLM-powered data normalization, metadata extraction, or cleaning.
  • Ability to own ingestion and normalization end-to-end with a focus on maintainability, reproducibility, and scalability.
  • Strong communication and cross-functional collaboration skills.
  • Familiarity with scientific data types such as plate-reader data, genomics metadata, time-series data, batch information, and instrumentation outputs is preferred.
  • Experience with workflow orchestration tools such as Nextflow, Prefect, Airflow, or Dagster is preferred.
  • Experience with cloud infrastructure, AWS S3, data lakes or warehouses, and database schemas is preferred.
  • Experience building or integrating LLM-based data transformation or cleansing agents is preferred.
  • Computational biology, laboratory data, or bioinformatics experience is a bonus but not required.

Benefits

  • Mission-driven opportunity to improve the quality and reliability of scientific data and AI systems.
  • High ownership and autonomy over ingestion architecture, standards, and pipelines.
  • Collaboration with a tight-knit team of engineers, scientists, and builders.
  • In-person role based in a San Francisco office.
  • Comprehensive PPO medical, dental, and vision coverage through Anthem.
  • 401(k) with top-tier plans.

Salary: $180k - $220k/yr

Mithrl

The scientific decision engine for R&D. Raw data → defensible decisions, faster. Pharma and biotech R&D often spend weeks, or even months, learning, coding, and troubleshooting discovery analysis pipelines. This slows research and diverts focus from the work that truly matters: discovery. Mithrl ...

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