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About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
1463 open roles
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The Deep Learning Product Research Engineer joins the Deep Learning Product Research Engineering team to bridge the gap between research, product engineering, and go-to-market strategies. This role involves building cutting-edge prototypes, evaluating emerging models, and creating technical content like white papers, benchmarks, and sample applications. You will develop enterprise-ready assets such as reference architectures and performance tuning recipes for Nemotron, NeMo, and NIM. Key responsibilities include leading product research for generative AI, evaluating agent technology, and converting field signals into structured product intelligence. Required skills include proficiency in Python, PyTorch, Hugging Face Transformers, LangChain, and LlamaIndex. You must possess experience in deep learning, fine-tuning models, and RAG pipelines to solve complex problems within the broader AI developer ecosystem and software framework.
Evaluate emerging generative AI models, agent technology, and reinforcement learning to assess impact on NVIDIA products.
Build proof-of-concept applications, benchmarks, and reference sample code to validate new product capabilities.
Convert customer and field signals into structured product intelligence, including adoption trends and roadmap recommendations.
Develop enterprise-ready enablement assets like reference architectures, integration playbooks, and performance tuning recipes.
Create reusable evaluation harnesses, profiling utilities, and agentic workflows to advance internal LLM expertise.
Distill research and engineering work into technical assets like white papers, code examples, and demos.
Translate research insights into concrete feature requests, launch inputs, and usability improvements for product teams.
What we're looking for
Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent experience.
5+ years of proven experience in software engineering, machine learning engineering, AI engineering, solutions architecture, applied research, or a similar technical role.
Hands-on experience building, training, fine-tuning, evaluating, deploying, or optimizing machine learning, deep learning, or agentic AI models and applications.
Practical experience with generative AI systems, including large language models, retrieval-augmented generation, agentic workflows, model evaluation, or AI application development.
Experience with Python and modern deep learning frameworks such as PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, or TensorFlow.
Familiarity with modern AI-assisted development tools and coding agents such as Codex, Claude Code, or Cursor.
Ability to create technically rigorous content for developers, including tutorials, blogs, sample code, white papers, benchmarks, or demos.
Strong communication skills to explain complex technical topics to both expert and non-expert audiences.
NLP
Machine Learning
Generative AI
LLM
SFT
RLHF
Data Synthesis
Parameter-Efficient Fine-Tuning
LLM-judge
ML Pipelines
Agentic Workflows
Data Science
Model Training
Model Probing
Tool Calling