About the job
NVIDIA is seeking a Senior Solutions Architect to drive innovation with healthcare and life sciences customers across North America, focusing on GPU-accelerated simulations for clinical sciences and autonomous labs. As a pioneer in accelerated computing, NVIDIA empowers pharmaceutical, biotech, and healthcare organizations to unlock new possibilities in patient modeling, laboratory and biomanufacturing robotic systems, and multi-agent reasoning. In this role, you will partner with leading pharmaceutical companies, techbios, and software builders to design, implement, and optimize GPU-accelerated AI software. If you are passionate about pushing the limits of accelerated computing in life sciences, we want to hear from you!
Responsibilities
Guide customers through the end-to-end adoption of GPU-accelerated AI, from requirements gathering and proof-of-concept development to deployment, integration, and ongoing optimization.
Architect libraries such as GPU-accelerated solvers for quantitative systems pharmacology and CPU-to-GPU migration of scientific workloads.
Perform low-level CUDA optimization, including custom kernels to accelerate simulation and inference workloads in drug discovery
Building physical AI and robotics solutions for autonomous labs and biomanufacturing such as sim-to-real VLA pipelines, real-time control layers, and integration of perception, control, and policy stacks on NVIDIA platforms.
Designing and deploying biomedical agentic AI systems, such as graph-based retrieval, multi-hop clinical reasoning, and persistent agent memory
Keeping up to date on AI advancements in healthcare, including domain-specific models, robotics, and agentic frameworks.
Engaging with life science executives, IT leaders, data scientists, and developers to drive adoption of NVIDIA AI stack.
Sharing your findings through training sessions, white papers, blog posts, and conference talks.
Qualifications
Minimum
MS, PhD, or equivalent experience in Computer Science, Biomedical Engineering, Computational Biology, Computational Chemistry, Robotics, or related fields with strong applied experience.
8+ years of experience.
Proven track record in software development for AI/ML, scientific computing, GPU acceleration, or robotics applied to healthcare or life sciences.
Hands-on experience across at least two of the three focus areas: GPU-accelerated scientific simulation, sim-to-real robotics, and end-to-end agentic AI.
Proficiency in Python and AI/ML frameworks (PyTorch, LangChain, or custom). Experience with C/C++ and CUDA strongly preferred.
Experience deploying and scaling GPU-accelerated solutions in cloud or HPC environments (OCI, AWS, Azure, or on-prem clusters).
Excellent communication skills with the ability to present complex technical concepts to both technical and non-technical audiences.
Up to 20% travel may be required for on-site customer engagements.
Preferred
Experience building GPU-accelerated scientific solvers, including low-level CUDA kernel optimization.
Background with sim-to-real robotics for life sciences—autonomous labs, biomanufacturing, surgical/clinical platforms—including MuJoCo or Isaac Sim, VLA pipelines, real-time control layers, and depth/RGB perception stacks.
Experience building, deploying, and evaluating agentic AI systems for healthcare—graph RAG over biomedical literature, long-memory agents, vision-based clinical event detection in production.
Familiarity with NVIDIA libraries and platforms