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# Senior Software Engineer, Generative AI Research

- **Company:** NVIDIA Ltd.
- **Location:** Santa Clara, CA, United States
- **Experience:** Expert
- **Salary:** $184,000.0 - $356,500.0
- **Contract:** Permanent contract
- **Skills:** C++ (Programming Language), Computer Engineering, Distributed Systems, Python (Programming Language), Open Source Technology, Data Processing, Reliability of Systems, Generative AI, Information Technology, Data Management, Machine Learning Operations, Data Pipelines
- **Published:** June 5, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=8418d3ba462dd367

## About the Role

Do you have experience in Scalable systems?, Do you have a Master's degree?, * A Masters Degree in Computer Science, Computer Engineering, related STEM Degree, or equivalent experience.

 * Strong engineering background in distributed systems, ML infrastructure, or large-scale compute/data platforms with 6 years of relevant work experience
 * Proficiency in Python and at least one systems language (e.g., C++/Go/Rust)
 * Experience with orchestration systems, scheduling, and scalable storage or data pipelines
 * Ability to work across teams, drive technical clarity, and deliver robust solutions in complex environments
 * Comfortable bridging research workflows and production-grade systems

Ways to Stand Out from the crowd:

 * Experience building or optimizing infrastructure for large-scale model training
 * Hands-on work with distributed compute environments or high-performance systems
 * Familiarity with synthetic data, simulation pipelines, or large multimodal datasets
 * Contributions to open-source infrastructure or large-scale internal tooling

## Description

* Design, build, and operate scalable infrastructure for training Cosmos and supporting large-scale data pipelines
 * Develop high-throughput systems for data processing, retrieval, and workflow orchestration
 * Collaborate across research, optimization, and platform teams to accelerate experiments and deployments
 * Improve system reliability, performance, and observability across distributed compute environments
 * Contribute to long-term infrastructure strategy for training, data management, and large-scale compute efficiency


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