Senior Solutions Architect, Simulations - Clinical Sciences and Autonomous Lab

We are 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 organisations to unlock new possibilities in patient modelling, 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 optimise GPU-accelerated AI software.

Key responsibilities include:

• Guiding customers through the end-to-end adoption of GPU-accelerated AI, from requirements gathering and proof-of-concept development to deployment, integration, and ongoing optimisation.

• Architecting libraries such as GPU-accelerated solvers for quantitative systems pharmacology and CPU-to-GPU migration of scientific workloads.

• Performing low-level CUDA optimisation, 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.

Requirements include:

• A Master's degree, 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.

• A 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.