AI Bioinformatics Engineering Lead
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AI Summary
Lead the evaluation and benchmarking strategy for AI-powered genomic data interpretation and validation. Own the mapping logic for genes, diseases, and variants while defining accuracy thresholds and release criteria for production.
The Position
The AI Bioinformatics Engineering Lead will sit within the AI team and collaborate closely with backend engineering, bioinformatics, and product leaders to shape how genomic data is evaluated and validated. Your success will be measured by robust evaluation and benchmarking strategies, clear accuracy thresholds, and production releases that consistently meet predefined correctness standards over time.
The Impact
- Own gene, disease, and variant mapping logic in the AI system.
- Design and maintain how genomic data is used and interpreted in AI workflows.
- Define approaches for handling complex and edge-case genomic scenarios.
- Set measurable accuracy thresholds for AI-powered features.
- Define release criteria that AI features must meet before going live.
- Lead the overall evaluation and benchmarking strategy for AI outputs.
- Establish regression standards so future changes preserve or improve accuracy.
- Partner with the AI Bioinformatics Benchmarking Engineer on automated validation.
- Approve validation criteria that determine when AI features are ready for release.
- Work closely with AI, Bioinformatics, Product, and Engineering leaders on system design.
- Recommend where deterministic logic should supplement model behavior in production.
- Ensure interpretation logic can be maintained and updated as new genomic knowledge appears.
Dominant and Recessive Traits
- Advanced degree in Bioinformatics, Computational Biology, or a closely related field.
- Strong experience with VCFs and complex genomic datasets.
- Familiarity with ClinVar, dbSNP, HGVS standards, transcript databases, and variant classification.
- Systems-level leadership that defines standards and frameworks for correctness.
- Experience working with production software engineering teams.
- Experience collaborating with AI or LLM-based systems.
- Experience setting or contributing to evaluation and benchmarking frameworks.
- Comfortable working fully remote as part of a distributed team.
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