Solutions Architect - Deep Learning for Drug Discovery
NVIDIA is seeking a Solutions Architect to join our EMEA team, focused on redefining the biopharma industry through AI-powered drug discovery. As a trusted technical advisor, you will collaborate with leading pharmaceutical companies, biotechs, and research organizations to accelerate breakthroughs in healthcare using NVIDIA’s computing platform. Our solutions architects are developers and scientists who thrive in a fast-paced environment, applying deep learning, machine learning, and high-performance computing to solve complex scientific challenges. You will play a pivotal role in driving innovation by building proof-of-concept demonstrations, scaling AI deployments, and fostering relationships with key customers. This position offers the opportunity to work on groundbreaking projects that are revolutionizing how drugs are discovered and developed. If you are passionate about AI and computational science and have a background in life sciences or related fields, we invite you to help shape the future of healthcare with NVIDIA.
What you will be doing:
- You will partner with our business / account team working with customers to develop a keen understanding of their goals, strategies, and technical needs as well as help to define and deliver high-value solutions meeting these needs.
- Staying up on the state of the art in the production deep learning and machine learning methods. You'll be called on to help architect and scale high-performance, distributed, AI deployments that are built on the latest NVIDIA GPU supercomputers.
- Document what you know and teach others. This can vary from building targeted training for partners and other Solutions Architects, to writing whitepapers, blogs, and wiki articles, to simply working through hard problems with a customer on a whiteboard.
- Be an industry leader with vision on integrating NVIDIA technology into AI and HPC architectures for advanced applications, such as foundation model training or autonomous labs.
- Strategically partner with lighthouse customers and industry-specific solution partners targeting our computing platform.
- 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.
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you are passionate about NVIDIA technology and how it can unlock Digital Biology, we want to hear from you!
NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we 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.
What we need to see:
- MS or PhD (or equivalent experience) in Computer Science, Computational Biology, Computational Chemistry or Computational Physics with strong applied experience in these domains.
- 5+ years of work-related experience in software development of deep learning or GPU acceleration methods for scientific applications.
- 3+ years of work-related experience with deep learning software architecture and frameworks or high performance computing applications.
- Proficient in the Linux/GNU toolchain and operating as a user in HPC cluster environments.
- Full-stack scientific computing experience including software development in scientific programming languages, such as Python, C/C++, and/or CUDA.
- Excellent communication skills particularly in the presentation of highly technical material.
- Must enjoy interacting with forward-thinking people, life-long learning, and staying at the forefront of the domain
Ways to stand out from the crowd:
- Demonstrated work in optimizing training and inference at scale and expertise in accelerated computing with GPU.
- 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., using CUDA), or experience with distributed programming models for supercomputing applications, AI deployment/inference technologies (e.g. TensorRT) or optimization frameworks (e.g. cuOpt), is a plus.
- Experience developing deep learning models using clinical trial or real‑world patient data.
- Experience in the pharmaceutical industry or established thought leadership through publications or presentations on AI/ML applications in healthcare and life science.
