NVIDIA is looking to redefine its engineering flows by leveraging generative AI and agentic workflows to improve future chip designs, essentially using AI to design next-generation AI systems.
Requirements
- Deep practical knowledge of LLMs, DL/ML, and Agent development.
- Well versed in agentic literature and eager to continue learning.
- Strong background in implementing AI solutions to solve real-world engineering problems.
- Experience with training/fine-tuning custom models, building multi-agent systems, retrieval augmented generation (RAG) pipelines, and vector databases.
- Background in computer architecture or hardware development.
- Good understanding of distributed systems and microservice architecture.
- Hands-on experience with NVIDIA Inference Microservices (NIMs).
Responsibilities
- Serve as an expert in implementing and deploying AI applications based on large language models (LLMs), internal and external agentic frameworks, and custom models.
- Work with hardware architects to identify how to best design, customize, and deploy AI-based solutions to their specific problem domains.
- Collaborate with infrastructure engineers to improve existing automated workflows by incorporating LLMs and establishing best practices for future solutions.
- Develop and optimize retrieval and generation algorithms for enterprise data (text, code, and images) to build advanced AI applications.
- Interact with internal research groups on how to solve complex chip design problems in new ways by leveraging machine learning (ML) and deep learning (DL).
- Research emerging AI technologies and engineering best practices to continuously evolve our development ecosystem and maintain a competitive edge.
Other
- MSc or PhD in Data Science, Computer Science/Engineering, Electrical Engineering, or equivalent experience.
- 5+ years of industry or research experience.
- Strong analytical, communication, and interpersonal skills.
- LI-Hybrid
- Applications for this job will be accepted at least until September 22, 2025.
