QA Engineer, Scientific Workflows

Mithrl•San Francisco, CA

1d•Onsite

About The Position

We are hiring a QA Engineer, Scientific Software to build the test, validation, and monitoring infrastructure that guarantees the correctness and reliability of the Mithrl AI Co-Scientist. This role requires a PhD-level scientist or computational biologist who understands the drug development lifecycle and who has hands-on experience with omics data. Without this scientific foundation, it is not possible to evaluate whether Mithrl’s outputs are biologically meaningful. You will create automated tests for analysis workflows, ingestion pipelines, and discovery applications. You will build CI systems that catch regressions early, set up monitoring and alerting for system behavior, and ensure that every module in Mithrl produces scientifically valid and reproducible outputs. This role bridges scientific understanding with software quality engineering and is critical for maintaining trust in Mithrl’s analysis engine. If you are a scientist with a passion for product reliability, reproducibility, and validation of ML powered scientific tools, this is a uniquely impactful position.

Requirements

PhD in biology, computational biology, bioinformatics, systems biology, or a related discovery field
Deep understanding of the drug discovery and preclinical development lifecycle
Hands-on experience working with omics data such as transcriptomics, RNA-seq, proteomics, ATAC-seq, single cell datasets, or imaging-derived features
Ability to evaluate whether a result, analysis, or insight is scientifically correct based on domain knowledge
Familiarity with common discovery analyses such as differential expression, enrichment, pathway reasoning, target scoring, and feature importance
Experience with Python or similar languages and comfort with scientific computing workflows
Strong interest in software quality, reproducibility, and validation of ML driven scientific systems
Excellent communication skills and ability to partner with engineers and scientists

Nice To Haves

Experience building automated tests or QA frameworks for scientific or ML systems
Experience with workflow engines, scientific pipelines, or reproducibility tools
Familiarity with CI tools and modern software development practices
Experience validating outputs of AI powered analysis tools
Previous work in a tech bio company or computational platform environment

Responsibilities

Build automated test infrastructure for data ingestion, analysis modules, discovery applications, and new product features
Develop scientific validation frameworks that check correctness and reproducibility of ML driven biological analyses
Establish CI workflows that run end to end tests on every commit and catch scientific and computational regressions
Build monitoring and alerting systems that track the health, performance, and scientific integrity of product modules
Design automated checks for omics workflows
Validate that responses generated by Mithrl align with biological logic and expectations from discovery and preclinical development
Work closely with ML engineers, data engineers, and application scientists to ensure scientific accuracy across releases
Maintain documentation, data fixtures, and gold standard datasets for regression testing
Support the development of QA processes that scale with rapid product growth and new analysis capabilities
Build a culture of scientific correctness and software reliability throughout the engineering and product teams