Senior Applied AI Bioinformatician

The opportunity

As Senior Applied AI Bioinformatician, you will discover and prove new genetically enabled capabilities for Sequencing’s products and broader intelligence layer.

Reporting: This role reports to the Senior Director, AI & Emerging Technologies.

This is a hands-on senior individual-contributor role for a genuinely strong bioinformatician who thinks and builds agentically. You will explore new ways to combine genetic data, scientific knowledge, retrieval tools, computational methods, and AI reasoning to create useful capabilities that are not possible today.

Examples could include a new bioinformatics skill, a genomic retrieval or interpretation component, or an evidence-synthesis workflow that expands what Sequencing’s AI products can do.

You will take promising ideas from scientific hypothesis through working prototype. For each proposed bioinformatics capability, you will define the user value, required data, underlying genetic premise, scientific uncertainty, evaluation plan, and evidence needed to advance, narrow, or stop the work. You will partner with Product and Engineering to turn validated ideas into scalable capabilities, while independent scientific evaluation remains a separate release safeguard.

This is a 0-to-1 exploration role, not the ongoing owner of production model quality. Bioinformatics retains ownership of benchmark maintenance, independent scientific evaluation, ongoing quality monitoring, release readiness, and scientific reliability. Your job is to open new possibility space, prove or disprove ideas quickly, and hand off evidence-backed capabilities for independent evaluation and productionization.

What you’ll own

• A portfolio of bounded exploratory projects that test new bioinformatics capabilities for Sequencing’s AI products and broader intelligence layer.
• Scientific hypotheses that connect a meaningful user need to a defensible genetic premise and an achievable computational approach.
• Hands-on prototypes combining genomic data, scientific literature and knowledge, retrieval or query tools, deterministic methods, and model reasoning.
• Explicit uncertainty boundaries, contraindications, data limitations, failure modes, and stop conditions for each proposed capability.
• Diagnosis of failures across source data, bioinformatics processing, retrieval and tool use, model reasoning, and orchestration during exploration and prototyping.
• Evidence packages that allow Product, Engineering, Bioinformatics, and independent evaluators to decide whether a capability should stop, be revised, or advance into independent evaluation.
• Clear handoffs to the AI Platform PM, Engineering, and Bioinformatics when a prototype has enough evidence to enter independent scientific evaluation and potential productionization.
• Ongoing awareness of Sequencing’s bioinformatics systems and capabilities through deep working partnership with the Bioinformatics team.
• Use of evaluation findings, customer questions, scientific developments, and production evidence as inputs to new hypotheses and prototype revisions, without owning the production quality loop.

What you will not own

• Ongoing ownership of production model quality, benchmark datasets, independent scientific evaluation, release readiness, or production scientific reliability. These remain with Bioinformatics and the designated quality owner.
• Independent scientific evaluation or final validation of your own work.
• Production release approval for scientific accuracy or safety.
• General backend-engineering delivery that belongs in the separate AI Bioinformatics Engineering search.
• Broad Bioinformatics organizational leadership or people management.

Who you are

• You have deep professional expertise in bioinformatics, computational genomics, statistical genetics, or a closely related field.
• You can evaluate the genetic premise, evidence quality, uncertainty, and limitations behind a proposed capability.
• You build hands-on with modern AI tools and can show how you have used models, agents, retrieval, tools, structured data, or computational pipelines to investigate or prototype scientific work.
• You understand that using AI is different from building a reliable AI system, and can explain validation, guardrails, failure handling, and human review.
• You can distinguish failures in data availability, bioinformatics processing, retrieval, model reasoning, and orchestrat…