Full-Time

Senior Solutions Architect

Simulations, Clinical Sciences and Autonomous Lab

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI HPC platforms

Compensation Overview

$184k - $356.5k/yr

+ Equity

Company Historically Provides H1B Sponsorship

Remote in USA + 2 more

More locations: California, USA | Massachusetts, USA

Remote

Category
Sales & Solution Engineering (2)
,
Required Skills
Microsoft Azure
Python
CUDA
PyTorch
AWS
LangChain
C/C++

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Requirements
  • 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.
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.
Desired Qualifications
  • 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

NVIDIA designs and manufactures graphics processing units (GPUs) and computing platforms used for gaming, data centers, and artificial intelligence. These products work by using parallel processing to handle complex mathematical calculations much faster than standard computer processors, supported by a software ecosystem that allows developers to build and run AI models. Unlike competitors that may focus solely on hardware, NVIDIA integrates its chips with specialized software and cloud services to create a complete environment for high-performance tasks. The company’s goal is to provide the underlying technology necessary to power advanced computing, from realistic video game graphics to autonomous vehicles and large-scale data analysis.

Company Size

10,001+

Company Stage

IPO

Headquarters

Santa Clara, California

Founded

1993

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Simplify Jobs

Simplify's Take

What believers are saying

  • Rubin delivers 5x faster inference and 3.5x faster training than Blackwell starting H2 2026.
  • Major hyperscalers Microsoft, AWS, Google Cloud, and CoreWe confirmed Vera Rubin implementation ahead of Q3 2026.
  • Rubin Ultra targets 15 ExaFLOPS FP4 inference with 1.5 PB/s NVLink bandwidth per rack in 2027.

What critics are saying

  • HBM4 scarcity from SK Hynix and Micron forces Rubin production cut to 1.5M units in 2026.
  • Kyber NVL144 rack delayed to 2028 due to TSMC 78-layer PCB yield failure, breaking annual cadence.
  • Rubin Ultra cuts HBM4E stacks to 12-Hi, delivering only 2.66x instead of 4x performance gain.

What makes NVIDIA unique

  • Vera Rubin is a six-chip extreme codesigned AI supercomputer platform, not just a GPU.
  • NVIDIA shifted to annual architecture cadence with Rubin, Ultra, and Feynman releases through 2028.
  • Vera CPU with 88 ARM cores enables per-GPU efficiency and 1/10 Blackwell operational costs.

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Benefits

Company Equity

401(k) Company Match

Growth & Insights and Company News

Headcount

6 month growth

0%

1 year growth

-2%

2 year growth

-3%
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