Solutions Architect - Healthcare and Life Sciences

About NVIDIA

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA is recruiting a Solutions Architect - Healthcare and Life Sciences to innovate in AI-driven drug discovery within the biopharma industry. You will collaborate with researchers to accelerate breakthroughs by building robust data strategies and AI models on NVIDIA’s computing platform. This role focuses on designing scalable ETL pipelines, curating high-quality scientific datasets, and implementing advanced machine learning workflows. Our solutions architects are elite developers and scientists who thrive in a fast-paced environment, applying deep learning, machine learning, and high-performance computing to solve complex scientific challenges. Candidates should have strong domain expertise in computational sciences and life sciences, with significant data engineering and deep learning experience. 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 researchers and scientists to develop a keen understanding of their data challenges and scientific goals, helping to define and deliver high-value data strategies that drive AI innovation.
• Staying up on the state of the art in Generative AI and data engineering for scientific applications.
• You will architect and implement high-performance ETL (Extract, Transform, Load) pipelines and data curation workflows to feed large-scale AI models on NVIDIA GPU supercomputers.
• Be an industry leader with a vision of integrating NVIDIA technology into the end-to-end data lifecycle—from raw scientific data ingestion to model training and inference.
• Building, optimizing, and deploying complex machine learning and deep learning training workflows, ensuring data efficiency and model performance at scale.

What We Need To See

• MS or PhD in Computational Biology, Computational Chemistry, Computational Physics, Chemical Engineering, Biophysics, or Computer Science with strong applied experience in these domains (or equivalent experience).
• 3+ years of work-related experience in data science, machine learning engineering, or computational science.
• Proficient in scientific Python (Pandas, NumPy, Scikit-learn) and deep learning frameworks (PyTorch, TensorFlow). Experience with workflow management tools (e.g., Airflow, Nextflow) is essential.
• Strong background in molecular modeling, life sciences, and familiarity with biological data formats (e.g., SMILES, PDB, FASTA).
• Excellent communication skills, particularly in the presentation of highly technical data strategies. Must enjoy interacting with forward-thinking people and bridging the gap between data engineering and scientific research.

Ways To Stand Out From The Crowd

• Demonstrated work in processing scientific data at scale (Terabyte/Petabyte scale) for cheminformatics, bioinformatics, or drug discovery.
• Experience preparing datasets for Large Language Models (LLMs), Generative AI, or geometric deep learning models in a scientific context.
• Experience in the biopharma industry or with customers/partners in the pharmaceutical domain, specifically with data governance and security.
• Published record of thought leadership in data science, AI, or a related industry segment.

Key skills/competency

• Artificial Intelligence (AI)
• Deep Learning
• Machine Learning
• ETL Pipelines
• Data Engineering
• Computational Biology
• Drug Discovery
• PyTorch
• TensorFlow
• Scientific Python
• High-Performance Computing (HPC)