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Bioptimus Logo Bioptimus · Research and Development

Biology Data Quality Engineer

· Job Posted February 16, 2026
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Job Description

Bioptimus is building the first universal AI foundation model for biology to fuel breakthrough discoveries and accelerate innovation in biomedicine. We are looking for a meticulous and detail-oriented Biology Data Quality Engineer to ensure the integrity and usability of the various and complex datasets that are central to our mission. In this critical role, you'll leverage your expertise in biology, data science, and machine learning to ensure the quality and consistency of biological data used to train and evaluate our foundation models.

Job Responsibility

  • Data Validation Pipeline Development: Develop and implement comprehensive data validation protocols for diverse biological datasets (histology, omics, clinical)
  • Ensure data integrity, consistency, and accuracy through rigorous quality checks
  • Design and implement automated data quality pipelines
  • Data Curation & Standardization: Establish and enforce data standardization practices
  • Curate datasets to enhance their usability for machine learning
  • Collaboration & Communication: Work closely with the R&D team to understand data requirements and address data quality concerns
  • Communicate data quality findings and recommendations effectively
  • Communicate and synchronize with external data providers
  • Documentation & Reporting: Maintain a detailed documentation of the data-quality assessment procedures, validation results, and data specifications
  • Generate regular reports on data quality metrics and trends
  • Data Source Evaluation: Evaluate and validate external public data sources
  • Continuous Improvement: Stay up-to-date with the latest data quality best practices and tools
  • Propose and implement improvements to our data-quality assessment processes and pipelines

Requirements

  • MSc in Biology, Computational Biology, Bioinformatics
  • Deep understanding of transcriptomics data types (bulk, single-cell, spatial) and their specific quality considerations
  • Good knowledge of genomics and proteomics data
  • Proven experience in implementing data quality control procedures and pipelines
  • Familiarity with data validation tools and techniques
  • Strong analytical and problem-solving skills
  • Proficiency in Python
  • Good knowledge of data visualization libraries (e.g. matplotlib)
  • Excellent written and verbal communication skills

Nice to have

  • Computational Pathology Data Expertise: Experience in machine learning analysis of histology images
  • Cloud expertise: Experience working with AWS
  • Data Annotation Experience: Experience with developing and implementing data annotation guidelines and processes
  • Experience with data ontologies
  • Proven experience building or contributing to large-scale data collections (e.g. Human Cell Atlas)
  • Spatial alignment of multimodal datasets (e.g. alignment between different imaging modalities)

What we offer

  • Competitive salary and equity package
  • Flexible work arrangements, including remote options
  • Opportunities for professional growth and leadership development
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