Senior Solutions Architect – Simulations, Clinical Sciences, Autonomous Lab

NVIDIA

Senior Solutions Architect driving innovation with GPU-accelerated simulations for clinical sciences and autonomous labs. Collaborating with pharmaceutical companies and software builders for AI implementation and optimization.

Posted 5/16/2026full-timeRemote • California, Massachusetts • 🇺🇸 United StatesSenior💰 $184,000 - $356,500 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudPythonPyTorch

About the role

Key responsibilities & impact

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 findings through training sessions, white papers, blog posts, and conference talks.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Eligible for equity and benefits

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

CUDAPythonC/C++AI/ML frameworksGPU accelerationscientific computingroboticsbiomedical AI systemsquantitative systems pharmacologycustom kernels

Soft Skills

communication skillspresentation skillscollaborationengagement with executivestrainingwritingpublic speakingproblem-solvingadaptabilityleadership