Senior Software Development Engineer, Neural Graph Infrastructure

at Nvidia

USD 168,000-310,500 per year

SENIOR

✅ On-site

Tech Stack
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1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI @ 4 Algorithms @ 7 Communication @ 7 Data Structures @ 7 LLM Machine Learning @ 6 Python @ 4 Software Development @ 7

Details

NVIDIA's CAD Infrastructure team is building a next-generation software platform for semiconductor design, applying graph neural network infrastructure to process graphs at a trillion-edge scale. The role involves contributing hands-on across architecture, implementation, and evolving engineering workflows.

Responsibilities

Architect highly automated and customizable flows using modular builds and object-oriented techniques.
Collaborate with architects and designers during active specification development to identify integration gaps and keep workstreams aligned.
Build event-driven data preprocessing pipelines for large-scale graph data.
Develop tools and user interfaces in C++ and Python for complex engineering workflows.
Engage cross-functionally with VLSI organizations to anticipate future challenges and build solutions.

Requirements

8+ years of relevant software engineering experience.
Bachelor's or master's degree in Computer Science or Computer Engineering, or equivalent experience.
Practical experience with C++ and Python programming.
Strong foundations in software development, algorithms, and data structures.
Proficiency in software architecture, including experience building complete systems.
Strong communication skills and comfort working across disciplines during an evolving development cycle.

Preferred Qualifications

Background in graph-scale data infrastructure or graph neural network-based systems.
Exposure to machine learning or agentic tooling, particularly large language model-based code generation.
Familiarity with electronic design automation or VLSI build environments.
Experience exposing software toolchains to AI agents or building developer-facing AI interfaces.