Deep Learning Product Research Engineer

at Nvidia
📍 Santa Clara, United States
USD 136,000-253,000 per year
MIDDLE
✅ On-site

Used Tools & Technologies

GenAI

Required Skills & Competences

Marketing @ 3 Python @ 3 Machine Learning @ 3 TensorFlow @ 3 Communication @ 6 Product Management @ 3 LLM @ 3 PyTorch @ 3 CUDA @ 3 Codex @ 2 Claude Code @ 2 Deep Learning @ 3 Generative AI @ 3 AI @ 3 Reinforcement Learning @ 3 Profiling @ 3 Agentic AI @ 3 RAG @ 3 TensorRT @ 3 LangChain @ 3

Details

NVIDIA's Deep Learning Product Research Engineering (PRE) team sits at the intersection of research, product engineering, and go-to-market. PRE reduces uncertainty about what will make products succeed by delivering working cutting-edge prototypes, product intelligence, and code-backed guidance that shape NVIDIA products and how customers adopt them.

Responsibilities

  • Lead product research for generative AI by evaluating emerging models, agent technology, reinforcement learning, and evaluation methods, then assessing what they mean for NVIDIA products.
  • Build proof-of-concept applications, benchmarks, and reference sample code that validate new capabilities and demonstrate product value.
  • Convert customer, developer, benchmark, usage, and field signals into structured product intelligence (adoption trends, friction points, issue reproductions, roadmap recommendations).
  • Develop enterprise-ready enablement assets such as reference architectures, integration playbooks, performance tuning recipes, and demo-to-production workflows for Nemotron, NeMo, NIM, and related NVIDIA AI software.
  • Partner with research, engineering, product management, technical marketing, field teams, and customers to turn insights into feature requests, launch inputs, positioning, and usability improvements.
  • Advance internal LLM expertise and tooling through reusable evaluation harnesses, profiling utilities, agentic workflows, and practical analysis of model behavior.
  • Produce technical assets from hands-on research and engineering work: code examples, technical write-ups, white papers, demos, talks, and patents where appropriate.
  • Stay current with advances in model training, post-training, inference, agentic systems, evaluation, deployment, safety, and the broader AI developer ecosystem.

Requirements

  • 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 with machine learning, deep learning, or agentic AI, including building, training, fine-tuning, evaluating, deploying, or optimizing models and AI 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 and libraries such as PyTorch, Hugging Face Transformers, LangChain, LlamaIndex, TensorFlow, or similar tools.
  • Familiarity with modern AI-assisted development tools and coding agents such as Codex, Claude Code, Cursor, or similar systems.
  • Ability to create clear, accurate, technically rigorous, and compelling content for developers (tutorials, blogs, sample code, white papers, benchmarks, demos).
  • Strong communication and presentation skills, with the ability to explain complex technical topics to both expert and non-expert audiences.

Ways to stand out

  • PhD in Computer Science, Engineering, Machine Learning, Artificial Intelligence, or a related field.
  • 3+ years of hands-on experience specifically with machine learning, deep learning, generative AI, large language models, multimodal models, reinforcement learning, model optimization, or agentic applications.
  • Experience designing or evaluating agentic AI systems, AI coding assistants, model evaluation harnesses, RAG pipelines, synthetic data workflows, or AI safety workflows.
  • Experience with NVIDIA AI software, models, or frameworks (NeMo, NeMo Retriever, NeMo Guardrails, NeMo RL, NIM, TensorRT, Dynamo, CUDA, cuDNN, Nemotron models).
  • Familiarity with the broader generative AI ecosystem, including open models, agent frameworks, vector databases, evaluation tools, deployment platforms, and emerging AI developer workflows.

Compensation and benefits

  • Base salary range (Level 3): 136,000 USD - 212,750 USD per year.
  • Base salary range (Level 4): 160,000 USD - 253,000 USD per year.
  • Eligible for equity and NVIDIA benefits (link to benefits noted in posting).

Additional information

  • Applications accepted at least until July 4, 2026.
  • NVIDIA uses AI tools in its recruiting processes.
  • NVIDIA is an equal opportunity employer and committed to fostering an inclusive work environment.