10 hours ago
Bengaluru, IndiaMid Level
Responsibilities
- Design and maintain automated chip performance verification pipelines for workload trace generation, multi-target test execution, results processing, and performance correlation.
- Develop performance monitoring and measurement infrastructure across simulation, emulation, and silicon environments.
- Build scalable data pipelines and full-stack dashboards to ingest, visualize, and compare performance metrics.
- Collaborate with GPU architects and hardware teams on verification infrastructure and workflow automation.
- Provide performance insights that support GPU architecture development and faster build iteration.
Requirements
- Bachelor’s or master’s degree in Computer Science, Computer Engineering, or equivalent experience.
- At least 3 years of software engineering experience, primarily using C++, with Python for tooling and automation.
- Strong computer science fundamentals in data structures, algorithms, object-oriented programming, and system design.
- Full-stack experience with Java or Python backends, REST APIs, and React or Ember is a plus.
- Proficiency with SQL or MongoDB for metrics storage and querying is a plus.
- Hands-on experience with Git, CI/CD, Kubernetes, Docker, and RabbitMQ is a plus.
- Exposure to GPU, CPU, or SoC chip performance verification is a plus.
- Familiarity with AI development tools and excellent interpersonal skills.
Benefits
- Hybrid work arrangement.
Categories
Data EngineeringTesting
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.