Senior Data Scientist

We seek a Senior Data Scientist to join our product-engineering data-science team and contribute to building agentic AI systems supporting NVIDIA's networking engineering workflows. You will create and implement production-ready LLM-powered solutions that collaborate closely with networking and silicon engineering teams. These solutions will transform complex domain expertise into scalable, measurable automation combining data science and applied AI.

What You'll Be Doing

• Build and ship agentic systems featuring multi-agent coordination, long-horizon reasoning, and planning frameworks that drive real efficiency gains for networking and NW silicon engineers.
• Build full-scale domain-specific agents and retrieval pipelines (RAG) over diverse data sources, including product telemetry, development artifacts, and engineering knowledge bases.
• Partner with networking SMEs to translate rules of thumb into structured agent instructions, evaluation datasets, and feedback loops.
• Establish evaluation infrastructure using offline golden sets and live telemetry, enabling confident iteration, cost control, and safe rollout of improvements.
• Analyze agent failures and data gaps, then propose and prototype improvements to features, algorithms, and system development.
• Lay the technical groundwork for a reusable agent platform that scales beyond your immediate team.
  
  

• PhD/MSc in CS, CE, Data Science, Statistics, or a related field (or equivalent experience).
• 5+ years in applied ML/AI or large-scale systems, with 2+ years building agentic or LLM-powered applications in production environments.
• Proficiency in Python; hands-on experience with agentic frameworks such as LangChain, LangGraph, CrewAI, or similar.
• Hands-on fine-tuning experience across full-weight, LoRA, and PEFT methods, with a solid grasp of key concepts including scaling laws and advanced alignment techniques (RL, RLHF, RLAIF, KL-divergence regularization, and related methods).
• Hands-on NLP experience before the LLM era includes BERT, word embeddings, sequence models, and classical NLP pipelines. You understand what is under the hood and see through the abstraction.
• Solid ML foundations: supervised learning, feature engineering, model evaluation, and statistical modeling.
• Strong communication skills — able to align diverse collaborators, document assumptions clearly, and drive progress without formal authority.
  
  

• Experience in networking or NW silicon domains — familiarity with network architectures, switch/NIC build flows, or related engineering concepts.
• Strong software engineering fundamentals with a track record of shipping production systems.
• Data engineering background: SQL/NoSQL, data pipelines, and working with large-scale structured and unstructured datasets.
• Ability to cut through complexity — knowing what matters, drilling into the right details, and keeping the business goal in sight.