Solutions Architect - AI 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:
- Support Business Development and Sales teams as part of a Team of 4, partnering with Industry Business leads, Account Managers, and Developer Relations managers to drive our developers’ ecosystem success.
- Work directly with developers and customers in a customer-facing setting
- Guide customers in implementing production grade inference deployments, model training, reinforcement learning (RL), and post-training algorithms by helping them adopt NVIDIA AI SDKs and APIs.
- Analyze application architectures and find opportunities for acceleration.
- Document what you know and teach others e.g. by building targeted trainings for partners and other Solutions Architects, delivering hackathons and technical demonstrations on NVIDIA solutions and platforms, writing whitepapers or blogs, or simply working through hard problems with a customer on a whiteboard.
- Explore the latest advancements in foundation model training and customization, agentic LLM applications and physical AI applications in biopharma.
- Provide feedback and collaborate with engineering, product and research teams.
- 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 accelerate drug discovery, we want to hear from you!
What we need to see:
- MS or PhD (or equivalent experience) in Computer Science, Computational Biology, Computational Chemistry or Biomedical Engineering with strong applied experience in these domains.
- 5+ years of experience in software development related to AI/ML in healthcare or life sciences.
- 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.
- Proficiency in Python and AI/ML frameworks (PyTorch, Langchain, or building custom framework).
- Experience developing, training and customizing Transformer models for healthcare and life sciences applications, especially using libraries like Transformer Engine or Megatron-LM.
- Excellent communication skills with the ability to present complex technical concepts to both technical and non-technical audiences.
- 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:
- Experience with large-scale pre-training and/or post-training of Transformer-based architectures for language, vision and/or multiple modalities.
- Experience deploying and scaling agentic AI solutions in cloud environments (AWS Bedrock, Azure AI foundry, Vertex AI, etc).
- Experience developing deep learning models using clinical trial or real‑world patient data.
- Hands-on experience with AI for physical systems, such as robotic instruments, autonomous labs, or intelligent devices, or with NVIDIA platforms (Metropolis, Cosmos, Omniverse, Isaac, CUDA-X)
- Experience in the pharmaceutical industry or established thought leadership through publications or presentations on AI/ML applications in healthcare and life science.
