Solution Architect – Digital Biology is a key technical role focused on advancing AI and high-performance computing in life sciences. In this position, you will partner directly with research institutions, biotech firms, and pharmaceutical organizations across EMEA to understand their scientific challenges and guide the integration of accelerated computing platforms into their workflows.
Key Responsibilities
- Lead technical engagements from concept to deployment, supporting customers through requirements analysis, proof-of-concept development, benchmarking, and optimization of AI-driven applications
- Collaborate with sales and engineering teams to align NVIDIA’s technology capabilities with customer objectives in areas such as multi-omics, protein modeling, and autonomous experimentation
- Design and implement AI solutions tailored to life science use cases, leveraging frameworks like PyTorch, LangChain, and Transformer-based models
- Build strong technical partnerships with software vendors and data platform providers to embed accelerated computing into scientific workflows
- Stay current with advancements in AI, computational biology, and large-scale model training, particularly in agentic systems and foundation models
- Share expertise through technical documentation, training sessions, whitepapers, and internal knowledge transfer
- Deliver clear, effective presentations of complex technical concepts to diverse audiences, including developers, researchers, and executive stakeholders
Qualifications
Applicants should hold an advanced degree (MS or PhD) in Computer Science, Computational Biology, Computational Chemistry, or a related field, with at least five years of applied experience in AI/ML within life sciences or healthcare.
Essential skills include proficiency in Python, hands-on experience with AI/ML frameworks, strong organizational abilities, and a demonstrated ability to communicate technical content clearly. Candidates must be self-motivated, enjoy problem-solving, and thrive in customer-facing technical roles.
Preferred Background
- Experience with large-scale AI applications such as protein structure prediction, drug discovery pipelines, or clinical data modeling
- Development and deployment of agentic AI systems in scientific contexts
- Customization and training of Transformer models using libraries like Transformer Engine or Megatron-LM
- Background in parallel computing, especially with CUDA, for accelerating scientific algorithms
- Familiarity with distributed computing models, cloud platforms (AWS, Azure), and inference optimization tools like TensorRT and cuOpt
- Industry experience in pharmaceuticals or with scientific software vendors
- Published work or public speaking on AI applications in life sciences
Work Environment
This role supports remote collaboration using conferencing tools, with occasional travel expected. You’ll work with cutting-edge GPU technology and contribute to transformative projects at the forefront of digital biology. The culture emphasizes technical leadership, continuous learning, inclusivity, and innovation in accelerated computing.
