Sr Global Molecular Omics Lead

We are passionate about what we do and driven by curiosity. We believe that learning and innovation are the foundation for transforming the future of agriculture. Our focus is on cutting‑edge genetic development, creating solutions that make a difference in the field and in the global market.

Our DNA not only guides us but also connects us: we work with empathy, transparency, and respect, ensuring agile and flexible operations. Here, you are the protagonist of your career, because we believe every individual plays an essential role in building something greater.

If you are passionate about learning and want to be part of evolving agriculture, GDM is the right place for you!

We are proud to offer a competitive benefits package, which includes:

• Medical, dental, and vision coverage
• 401(k) with company match (eligible 1^st of the month following date of hire)
• Generous vacation time & paid holidays
• Volunteer Time Off
• Paid parental leave
• Tuition and Health Club reimbursement
• Other benefits: Company paid Long-term and short-term disability insurance, company paid life insurance and optional supplemental life insurance, health savings account (HSA) with a company contribution and Flexible Spending Account (FSA)

GDM is an equal opportunity employer. We are proud to welcome candidates from all backgrounds and encourage you to apply.

Learn more about us at gdmseeds.com or follow us on LinkedIn and Twitter @GDMSeeds.

GDM is building an agentic, prescriptive breeding platform. The Computational Breeding & Molecular Omics Lead owns GDM's molecular omics science: integrating the molecular ‑omes (transcriptomics, genomics, epigenomics, metabolomics and related layers), driving next-generation genomics discovery, and designing GDM's next-generation marker panel — turning molecular signal into earlier, more prescriptive breeding decisions.

This role brings the molecular and computational-biology expertise GDM's breeding programs cannot generate on their own, and translates it into breeding value — delivering model-ready molecular features to the prediction stack and building the molecular applications that produce them. The role sits alongside the platform, prediction and agent-development functions: it owns the molecular science; the orchestration platform and the master AI agent are owned elsewhere.

• Integrate GDM's molecular ‑omes — transcriptomics, genomics, epigenomics, metabolomics and related layers — into coherent, selection-relevant molecular signal, rather than isolated single-omic analyses.
• Lead next-generation genomics discovery: evaluate and bring in the next wave of genotyping/sequencing and molecular approaches that raise resolution and lower cost.
• Design and own GDM's next-generation marker panel alongside the Biotech Lab — what it captures, at what density and cost, and how it improves genotyping across the pipeline; the prediction function validates the panel against accuracy.
• Keep improving GDM's genotyping and molecular capability.

• Identify which molecular signals — from WGS, transcriptomics or epigenomics — predict breeding value before phenotypic data is available, enabling earlier advancement decisions.
• Make Speed Breeding prescriptive: define the molecular markers and omics signals that allow selection in early generations, before field phenotype is measurable — the molecular contribution to GDM's Accelerated Cycle pillar.
• Define which molecular decision points can be automated and at what stage of the breeding cycle.

• Deliver all molecular data and features to the prediction function in model-ready format; if they improve accuracy they enter production, if not the role adjusts.
• Coordinate with Traits Co to generate molecular data to required specification (format, quality, developmental stage) so it is model-ready from the moment it is generated.
• Build production-grade molecular applications and bioinformatics pipelines — documented, version-controlled, API-connectable — not standalone analysis scripts; define each application's inputs and outputs so the master agent can call them.
• Molecular agents may be built in this role, but the master orchestration agent is owned by the AI Agent Engineer; this role owns the molecular logic, the platform decides integration.

• Act as a connector, cross-functional resource: take heterogeneous, disconnected data sources — molecular, genotypic, pipeline, phenotypic and external — and identify how they relate so they can be used together.
• Thread sources together into a coherent picture: map keys, resolve identifiers, reconcile formats and surface the relationships that let molecular data join genotype, phenotype and environment downstream.

• Data Analytics — feeds: model-ready molecular features and new-panel signal into the data platform. Fed back: data requirements, format/quality standards, and the pipelines and infrastructure on which the molecular applications run.
• Breeding Models (Prediction) — feeds: molecular features for the prediction models. Fed back: accuracy feedback — which features and panels improve prediction and which do not — which prioritizes discovery and panel design.
• AI Agent Engineer — feeds: molecular applications with clearly defined inputs and outputs (API contracts) so they can be orchestrated. Fed back: orchestration — the agent calls the molecular applications in the right sequence within the decision workflow.
• Traits Co — feeds: molecular data specifications (what to generate, at what quality and stage). Fed back: molecular data generated to specification.

• Strong molecular omics and computational-biology background across multiple layers — genomics plus at least one of transcriptomics / epigenomics / other ‑omes — applied to genetics or breeding.
• Demonstrated work in genomics innovation — marker/panel design, next-generation genotyping or sequencing, or integrating multiple molecular data types into usable signal.
• Production-grade computational skills (Python, R and/or HPC) — building pipelines and applications others use, delivered documented, reproducible, version-controlled and API-connectable.
• Ability to translate molecular findings into breeding implications — an earlier, more confident selection decision, not biological interest for its own sake.
• Preferred (strong plus): experience building AI agents or automated pipelines for genomic processes; Speed Breeding / early-generation molecular selection; row crops (soybean, corn, wheat, sunflower); commercial or AgTech environments where scientific output drives business decisions.

• PhD in computational biology, plant genomics, bioinformatics, plant genetics, or a closely related field.
• Track record delivering working software/tools that others use (not only analysis scripts), applied to plant genomics / molecular biology problems.
• Not required: end-to-end platform architecture or orchestration; ownership of the master orchestration agent; multimodal prediction modelling (genome+phenome+envirome); breeding-scheme design or simulation; prior commercial breeding-industry experience.

• Prolonged periods of working at a computer and using standard office / compute equipment.
• Frequent national and international travel to GDM research sites and partner facilities is required.