Solution Architect - Genomics

Join our team as a Solution Architect in EMEA, collaborating with innovative customers to accelerate real-world genomics use cases through advanced AI and accelerated computing. NVIDIA's AI and GPU technologies are redefining biological discovery-from genomics and single-cell workflows to transforming large-scale diagnostics and patient data modelling. Solve some of healthcare's most critical challenges at a company leading breakthroughs in computational biology, genomics medicine, and diagnostics.

We are seeking an enthusiastic individual to join us in exploring new opportunities in the AI-powered genomics revolution. As a Solution Architect, you will work as a trusted technical advisor to our customers, who envision accelerated computing and AI as a transformative force for genomics. Join us on this thrilling journey and advance your career while empowering leading organizations, industry, and institutions worldwide.

What you will be doing:

• Guide customers through the full journey of AI adoption and accelerated computing -from requirements gathering and proof-of-concept development to deployment, integration, benchmarking, and ongoing optimization.
• Collaborate with our business and account teams to identify technical needs, customer goals, and strategies. Your responsibilities will include enabling customer adoption of NVIDIA technology by mapping our solutions to their use cases and driving positive relationships with our technology partners, making NVIDIA an integral part of end-user solutions.
• Keep up to date with AI/ML for genomics innovations and the latest in accelerated computing technology for this field.
• Be a technical leader collaborating with leading research and industry teams to solve multiomics-including genomics-and foundation-model challenges at scale, leveraging NVIDIA technology.
• Engaging with developers, researchers, data scientists, IT managers, and senior leaders internally and externally is an essential part of the Solution Architect role to gain experience in various technical areas.
• Document solutions and deliver targeted training, whitepapers, and best practice guides for customers and partners. We make heavy use of conferencing tools, but some travel is required for this role.
• You are empowered to find the best way to get your job done and make our customers successful.

• MS, PhD, or equivalent experience in Computational Biology, Genomics, Computer Science, or related field with hands-on genomics or multiomics work.
• Deep genomics domain expertise in data processing (secondary and tertiary analysis) in genomics or single cell technologies, with 5+ years work-related experience in AI/ML and accelerated computing for related use-cases.
• Proven experience with Python and relevant AI/ML and domain frameworks (PyTorch, Nextflow/WDL, RAPIDS, Parabricks, scverse or building custom framework) and their application to scientific questions.
• Demonstrated strong time-management and organizational abilities in coordinating multiple initiatives and priorities, as well as implementing new technologies and products within highly complex projects.
• Motivated self-starter with strong problem-solving abilities and excellent customer-facing communication skills, particularly in presenting complex technical concepts effectively. Must enjoy engaging with innovative individuals, continuous learning, and staying at the forefront of the field

Ways to stand out from the crowd:

• Hands-on experience in the development and optimization of bioinformatics tools, deep-learning foundation models, data analysis, and profiling for multiomics-including genomics and single-cell.
• Strong background in large scale data processing and AI for genomics, including whole genome sequencing (WGS), diagnostic and variant interpretation, knowledge curation, and model explainability.
• Proficiency in building or integrating multimodal patient or cell-level foundation models across genomics, pathology, and clinical data.
• Experience with GPU acceleration, CUDA development, or benchmarking to scale model training and inference.
• Recognized contributions through publications, opensource projects, or community engagement in AI-driven genomics or Digital Biology.