Solutions Architect at NVIDIA

Taipei, , Taiwan -

Full Time

Start Date

Immediate

Expiry Date

15 May, 26

Salary

0.0

Posted On

14 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI, GPU, VLM, Digital Twin, Accelerated Analytics, Simulation, Deep Learning, Machine Learning, HPC, Omniverse, PyTorch, TensorFlow, Genomics, Proteomics, Drug Discovery, BioNeMo

Industry

Computer Hardware Manufacturing

Description

We are looking for a Solutions Architect to work with AI for Healthcare and Life Sciences in Taiwan, focused on academic society to adopt NVIDIA GPU platform on VLM, Digital Twin, AI, Accelerated Analytics, Simulation, Deep Learning or Machine learning technologies. NVIDIA’s platform for HPC/MIG and Omniverse (OM), deep learning and high-performance computing have already made a major impact on industry. At NVIDIA, our Solution Architects are drawn from elite developers and scientists who enjoy working with the latest GPU hardware and software. We need a passionate, hard-working, and creative individual to help us pursue more of these opportunities in fields. Your primary focus is on AI for Sciences with researchers and developers at universities, research institute and labs. What you'll be doing: Design and implement GPU‑accelerated AI solutions for genomics, proteomics, and drug discovery using BioNeMo, Parabricks, or related frameworks. Collaborate with pharmaceutical, biotech, and research partners to co‑develop digital‑biology workflows leveraging large‑scale foundation models. Build reusable pipelines for sequence analysis, molecular simulation, and generative design of biomolecules. Optimize performance and scalability on NVIDIA GPU clusters and cloud environments. Contribute to BioNeMo ecosystem growth through technical enablement, reference implementations, and partner collaboration. Lead proof‑of‑concept and production engagements in RNA analysis, structure prediction, and computational drug discovery. What we need to see: Master’s degree or Ph.D. in Computational Biology, Bioinformatics, Computer Science, or related field. 6+ years of experience in AI, computational biology, or life‑sciences R&D. Strong background in deep learning and model development with PyTorch or TensorFlow. Familiarity with bioinformatics workflows such as variant calling, structural prediction, and molecular docking. Demonstrated experience with GPU‑accelerated computing or HPC systems. Excellent communication and cross‑disciplinary collaboration skills. Ways to stand out from the crowd: Hands‑on experience with BioNeMo, Parabricks, or CUDA‑accelerated life‑sciences libraries. Knowledge of foundation models for biological sequences, molecular property prediction, or structure generation. Familiarity with pharmaceutical R&D data formats (FASTA, BAM/VCF, PDB). Demonstrated ability to lead technical initiatives or customer collaborations in life‑sciences AI. With competitive salaries and a generous benefits package, NVIDIA is widely considered to be one of the most desirable employers in the world. We have some of the most brilliant and talented people in the world working for us. If you are creative, autonomous and love a challenge, we want to hear from you. We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. #LI-Hybrid NVIDIA is the world leader in accelerated computing. NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Our work in AI and digital twins is transforming the world's largest industries and profoundly impacting society. Learn more about NVIDIA.

Responsibilities

The Solutions Architect will design and implement GPU-accelerated AI solutions for life sciences, focusing on genomics, proteomics, and drug discovery using frameworks like BioNeMo and Parabricks. Key tasks include collaborating with partners to co-develop digital biology workflows, building reusable pipelines, and optimizing performance on GPU clusters.