About the job
Do you want to be part of the team that brings innovative Artificial Intelligence (AI) from research to reality? We are looking for a Solutions Architect to join the AI Software Segment SA team. We specialize in the newest technology and advances in deep learning, Generative AI, and Cloud. The vision of the AI SW Segment team is to use our deep expertise, at the intersection of research and engineering, to guide and enable the successful adoption of NVIDIA AI software in the enterprise!
Responsibilities
A huge part of our work involves developing end-to-end AI solutions for enterprise use cases. We help customers adopt NVIDIA AI models and libraries by offering deep technical expertise.
Tackle sophisticated AI challenges by applying skills across the AI model lifecycle—from data processing and orchestration to training, post-training, reinforcement learning (RL), evaluation, and model optimization.
Support a broad model portfolio spanning LLMs, multimodal, retrieval, speech, content safety, and edge use cases.
Partner with enterprise customers in co-design engagements — understanding their data, evaluation criteria, and success metrics to deliver customized AI solutions.
As we work with customers across multiple industries, we help improve NVIDIA products and build creative solutions to overcome scaling challenges at the intersection of computer architecture, models, libraries, and AI applications.
Contribute to the wider organization and community by sharing your expert knowledge. This can vary from contributing to open-source projects and product engineering to publishing findings and delivering hands-on training.
Qualifications
Minimum
Strong foundational expertise, from a BS, MS, or Ph.D. degree in Engineering, Mathematics, Physics, Computer Science, Data Science, or similar (or equivalent experience).
5+ years of experience with AI frameworks such as PyTorch, JAX, or TensorFlow, and libraries like Hugging Face Transformers.
Proficiency in Python programming, software design, debugging, and performance analysis, with at least 5+ years of experience in a Linux environment.
Hands-on experience with AI model lifecycle, including evaluation, failure analysis, pre-training, post-training, RL, and model optimization.
Expertise in distributed computing methodologies, including model and data parallelism.
Experience with distributed computing tools, like SLURM and Kubernetes, for training and evaluating large models on GPUs.
Ability to learn fast and quickly adapt to change.
Clear written and oral communications skills with the ability to effectively collaborate with executives and engineering teams.
Preferred
Experience with and/or contributions to open-source NVIDIA AI libraries and models, particularly Nemotron, NeMo, NeMo Framework, NeMo-RL.
Hands-on experience with data curation and analysis for model post-training and RL.
Prior experience with AI model training techniques applied to multi-modal data (audio, image, and video).
Knowledge of NVIDIA GPU/CPU architecture and its impact on software performance.
Show willingness and ability to dig into unfamiliar territories to solve complex problems relying on experience from previous work.