Solutions Architect - Deep Neural Network Evaluation

NVIDIA’s Worldwide Field Operations (WWFO) team is looking for an AI focused Solution Architect with expertise in evaluation of neural networks and agentic pipelines. A candidate with understanding of modern evaluation datasets and tools allowing to work with key model builders / AI application developers across EMEA to improve core models as well as end to end agentic pipelines. As Solutions Architect, you will be the first line of technical expertise between NVIDIA and our customers. Your responsibilities will vary from working on proof-of-concept demonstrations, to driving relationships with key executives and managers to promote NVIDIA technologies.

This role focuses on the evaluation, analysis, and design of agentic AI systems, with a strong emphasis on LLMs and retrieval-based architectures. It involves benchmarking AI systems across diverse use cases and languages, evaluating performance across the end-to-end stack from LLMs and VLMs to embedding models. The position also requires deep analysis of system failures and the development of mitigation strategies, including model selection, retrieval pipeline optimization, and LLM or embedding model fine-tuning. We are looking for a candidate passionate about AI with strong expertise in neural network development and fine-tuning. The ideal candidate will be able to collaborate across customers, engineering teams, industry business development, and corporate marketing. In this role, you will work with the latest LLM, VLM, and embedding technologies that are redefining how people interact with intelligent systems.

What You’ll Be Doing:

• Work directly with key customers to understand their technology and provide the best solutions.

• Drive adoption of NVIDIA open-source evaluation tools (NeMo) and help the community to develop standard methodology for model's robustness measurement.

• Develop and demonstrate solutions based on NVIDIA’s and open-source LLM technology and integrate them into agentic pipelines.

• Partner with engineering, product and sales teams to develop, plan best suitable solutions for customers. Enable development and growth of product features through customer feedback and proof-of-concept evaluations.

• Build industry expertise and become a contributor in integrating NVIDIA technology into Enterprise Computing architectures.

What We Need to See:

• MS/PhD or equivalent experience in Computer Science, Data Science, Electrical/Computer Engineering, Physics, Mathematics, other Engineering fields

• 5+ years of hands-on experience in building, evaluating Deep Neural Networks.

• A proven record of academic and/or industry experience in fields related to machine learning, deep learning and/or data science with preference towards Deep Learning.

• Work experience and knowledge of the latest LLMs or VLMs and Retrieval Systems.

• Understanding of key libraries used for DNN evaluation like Eval Harness or NVIDIA Evaluation Microservice as well as agentic pipeline evaluation.

• Good understanding of modern evaluation benchmarks and ability to extend them / build new benchmarks.

• Excellent verbal, written communication, and technical presentation skills in English

Ways to Stand Out from The Crowd:

• Experience working on evaluating models whether that is in measuring memorization of models, security risks associated with hidden instructions, etc.

• Experience using evaluation tools with large models at scale.

• Understanding of large-scale distributed training.

• Experienced in evaluation approaches for reinforcement learning.

Widely considered to be one of the technology world’s most desirable employers, NVIDIA offers highly competitive salaries and a comprehensive benefits package. As you plan your future, see what we can offer www.nvidiabenefits.com/

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.