Solution Architect – Digital Biology
NVIDIA · France
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- Hybrid
- Full-time
- $150,000 / year
- France
Job highlights
- Advise customers on AI adoption in life sciences.
- Collaborate with business teams on technical strategies.
- Stay updated on AI and NVIDIA technologies.
- Lead technical vision for AI/HPC integration.
- Engage with diverse technical stakeholders.
About the role
Solutions Architect – Digital Biology
Join NVIDIA as a Solutions Architect in EMEA, collaborating with life sciences customers to accelerate digital biology use cases. Our team of exceptional developers and scientists works with the latest GPU hardware and software. NVIDIA's platform for ML/AI and high-performance computing has a significant impact on the healthcare industry, serving leading pharmaceutical companies, academic medical centers, and start-ups.
We are seeking an enthusiastic individual to explore new opportunities in the AI-powered digital biology revolution. As a Solution Architect, you will be a trusted technical advisor to customers in the pharmaceutical and techbio industries, viewing accelerated computing and artificial intelligence as transformative forces in their field. Advance your career while empowering top organizations and institutions worldwide.
What You Will Be Doing
- Guide customers through the end-to-end process of AI adoption—from requirements gathering and proof-of-concept development to deployment, integration, benchmarking, and ongoing optimization.
- Collaborate with our business/account team to identify technical needs, customer goals, and strategies. Enable customer adoption of NVIDIA technology by mapping our solutions to their use cases and driving positive relationships with technology partners, making NVIDIA an integral part of end-user solutions.
- Stay up-to-date on AI advancements in Digital Biology and relevant NVIDIA technologies enabling this innovation.
- Be a technical leader, bringing vision to the integration of NVIDIA technology into AI and HPC architectures for advanced applications like agentic AI, autonomous labs, or drug discovery.
- Engage with developers, researchers, data scientists, IT managers, and senior leaders internally and externally to gain experience in various technical areas.
- Document and teach others through targeted training for partners and Solutions Architects, writing whitepapers, blogs, and wiki articles, or working through problems with customers on a whiteboard.
- Utilize conferencing tools extensively, with some travel required. Find the best way to get your job done and ensure customer success.
What We Need To See
- MS or PhD (or equivalent experience) in Computer Science, Computational Biology, Computational Chemistry, or Computational Physics, or related fields with strong applied experience.
- 5+ years of work-related experience with hands-on expertise in AI/ML for healthcare or life sciences.
- Proven experience with Python and AI/ML frameworks (PyTorch, Langchain, or building custom frameworks) and their application to scientific questions.
- Strong time-management and organizational skills for coordinating multiple initiatives, priorities, and implementations of new technology and products into complex projects.
- Motivated self-starter with a balance of strong problem-solving and customer-facing communication skills, especially in effectively presenting complex technical information. Enjoy engaging with innovative individuals, continuous learning, and staying at the forefront of the field.
Ways To Stand Out From The Crowd
- Demonstrated work in AI-at-scale related to multi-omics foundation models, protein structure prediction, drug discovery, or clinical development.
- Experience building, deploying, and optimizing agentic AI systems for healthcare and life sciences, especially for scientific software vendors and data platforms.
- Experience developing, training, and customizing Transformer models for healthcare and life sciences applications, especially using libraries like Transformer Engine or Megatron-LM.
- Background with accelerating scientific algorithms using parallel programming (e.g., CUDA), distributed programming models for supercomputing, AI deployment/inference technologies (e.g., TensorRT), cloud deployment (AWS, Azure), or optimization frameworks (e.g., cuOpt).
- Experience in the pharmaceutical industry or established thought leadership through publications or presentations on AI/ML applications in healthcare and life science.
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. We highly value diversity in our current and future employees and do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status, or any other characteristic protected by law.
Key skills/competency
- Solutions Architect
- Digital Biology
- AI/ML
- Life Sciences
- Drug Discovery
- Python
- PyTorch
- Healthcare
- HPC
- CUDA
Skills & topics
- Solutions Architect
- Digital Biology
- AI
- ML
- Life Sciences
- Healthcare
- Python
- PyTorch
- HPC
- Drug Discovery
- Computational Biology
- CUDA
- EMEA
How to get hired
- Tailor your resume: Highlight AI/ML expertise in healthcare/life sciences and Python/framework experience.
- Showcase your experience: Emphasize your MS/PhD and 5+ years of relevant work.
- Demonstrate problem-solving: Detail your customer-facing communication and technical presentation skills.
- Highlight standout qualifications: Mention AI-at-scale, agentic AI, Transformer models, or CUDA experience.
- Research NVIDIA: Understand their commitment to diversity and innovation in digital biology.
Technical preparation
Master Python and AI/ML frameworks (PyTorch, Langchain).,Familiarize with CUDA and parallel programming.,Study AI-at-scale, agentic AI, and Transformer models.,Understand AI deployment and cloud technologies.
Behavioral questions
Describe a complex technical challenge you solved.,How do you explain technical concepts to non-experts?,How do you stay current with AI advancements?,Share an experience collaborating with diverse teams.
Frequently asked questions
- What specific AI/ML frameworks are most important for the Solutions Architect Digital Biology role at NVIDIA?
- The Solutions Architect Digital Biology role at NVIDIA requires proven experience with Python and AI/ML frameworks such as PyTorch and Langchain, or the ability to build custom frameworks. Familiarity with libraries like Transformer Engine or Megatron-LM for Transformer models is also highly valued, along with experience in AI deployment/inference technologies like TensorRT.
- What educational background is preferred for the Solutions Architect Digital Biology position at NVIDIA?
- NVIDIA prefers candidates with an MS or PhD (or equivalent experience) in Computer Science, Computational Biology, Computational Chemistry, or Computational Physics. A strong applied experience in these domains is crucial for this role.
- How much work experience is required for the Solutions Architect Digital Biology job at NVIDIA?
- For the Solutions Architect Digital Biology position at NVIDIA, a minimum of 5 years of work-related experience is required, with a focus on hands-on expertise in AI/ML for healthcare or life sciences.
- What are the key responsibilities of a Solutions Architect in Digital Biology at NVIDIA?
- Key responsibilities include guiding customers through AI adoption, collaborating with account teams, staying updated on AI advancements, leading technical vision for AI/HPC integration, and engaging with diverse technical stakeholders. Documenting knowledge and teaching others is also a significant part of the role.
- Does the Solutions Architect Digital Biology role at NVIDIA involve travel?
- Yes, while NVIDIA heavily utilizes conferencing tools, some travel is required for the Solutions Architect Digital Biology role to engage with customers and partners effectively.
- What kind of 'stand out' experience is NVIDIA looking for in Solutions Architect Digital Biology candidates?
- NVIDIA is particularly interested in candidates with experience in AI-at-scale for multi-omics, protein structure prediction, drug discovery, or clinical development. Experience with agentic AI systems, Transformer models, parallel programming (CUDA), AI deployment technologies, or cloud deployment is also highly advantageous.
- How can I best prepare my resume for the Solutions Architect Digital Biology role at NVIDIA?
- To prepare your resume for the Solutions Architect Digital Biology role at NVIDIA, strongly emphasize your AI/ML experience in healthcare or life sciences, proficiency in Python and relevant frameworks (PyTorch, Langchain), and your MS/PhD or equivalent experience. Highlight any specific projects or achievements related to drug discovery, agentic AI, or parallel programming.