The relevant domain is Computational Science and High-Performance AI for Drug Discovery.
The appropriate group of people to review this topic would be Senior AI/HPC Strategists and Computational Biology Leads.
Abstract
This document details the requirements for a Solutions Architect position at NVIDIA, focusing on the strategic acceleration of biopharma drug discovery through Deep Learning (DL) and the NVIDIA computing platform within the EMEA region. The role mandates a senior technical expert with an advanced degree (MS or PhD) in computational sciences (Biology, Chemistry, Physics, or Computer Science). The architect will serve as a trusted technical advisor, specializing in the design, scaling, and deployment of distributed, high-performance AI solutions on GPU supercomputers, including the integration of foundation models and the development of autonomous laboratory systems. Essential prerequisites include demonstrated expertise in GPU acceleration methodologies, scientific full-stack programming (Python, C/C++, CUDA), Linux/HPC environments, and a minimum of five years of experience in DL/GPU development for scientific applications.
Solutions Architect - Deep Learning for Drug Discovery (NVIDIA)
0:00 Strategic Role Definition: The Solutions Architect role is focused on the biopharma sector in EMEA, serving as a technical advisor to leading pharmaceutical, biotech, and research organizations to accelerate breakthroughs using NVIDIA's platform. The role requires applying expertise in Deep Learning (DL), Machine Learning (ML), and High-Performance Computing (HPC).
0:00 Core Responsibilities:
Solution Delivery: Collaborate with business teams to comprehend customer technical needs, goals, and strategies, subsequently defining and delivering high-value technical solutions.
Architectural Leadership: Design, architect, and scale high-performance, distributed AI deployments built on the latest NVIDIA GPU supercomputers.
Knowledge Transfer: Document and educate internal and external stakeholders through targeted training, whitepapers, blogs, and direct customer engagement.
Industry Vision: Act as an industry leader with a strategic vision for integrating NVIDIA technology into AI/HPC architectures for advanced applications (e.g., foundation model training, autonomous labs).
Customer Partnership: Strategically partner with key "lighthouse" customers and industry-specific solution providers.
Education: MS or PhD (or equivalent experience) in Computer Science, Computational Biology, Computational Chemistry, or Computational Physics, with substantial applied experience in these domains.
Experience: 5+ years in software development of DL or GPU acceleration methods for scientific applications, and 3+ years experience with DL software architecture, frameworks, or HPC applications.
Programming Stack: Proficiency in full-stack scientific computing, including Python, C/C++, and/or CUDA.
HPC Proficiency: Proficient operation within HPC cluster environments utilizing the Linux/GNU toolchain.
0:00 Differentiating Expertise (Ways To Stand Out):
Optimization/Scale: Demonstrated success optimizing training and inference at scale, specifically utilizing GPU accelerated computing.
Transformer Models: Experience developing, training, and customizing Transformer models for healthcare/life sciences applications, ideally using libraries such as Megatron-LM or Transformer Engine.
Parallel/Distributed Computing: Background in accelerating scientific algorithms using parallel programming (e.g., CUDA) or distributed programming models for supercomputing.
AI Deployment: Experience with AI deployment/inference technologies (e.g., TensorRT) or optimization frameworks (e.g., cuOpt).
Domain Leadership: Experience in the pharmaceutical industry or established thought leadership (publications/presentations) on AI/ML applications in life science.
0:00 Operational Note: The position is remote in Switzerland, requires some travel, and emphasizes strong communication skills for presenting complex technical material.