Senior System Level Integration Engineer

Reposted Yesterday
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Yokneam
In-Office
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
The Senior System Level Integration Engineer will manage internal releases, integrate engineering deliverables, and resolve system issues, ensuring quality and efficiency across teams.
Summary Generated by Built In

NVIDIA Networking business unit, IC silicon product engineering department is looking for a hardworking multi-disciplinary engineer to join a highly skilled existing team in bringing up new high end company products by implementing/maintaining its testing framework/infrastructure, assimilating new tests and validating its correctness, defining new features with target of reaching even higher quality in shorter time.

We are looking for a passionate and driven Integration Engineer to join our dynamic team. In this role, you will sit at the heart of the product lifecycle — interfacing between R&D, validation, and manufacturing teams. Your mission will be to ensure seamless integration of cross-team deliveries, manage internal releases, and resolve technical issues from lab to production. If you're a strong technical leader with hands-on lab experience and a sharp eye for system-level debugging, we want to hear from you.

What you’ll be doing: 

  • Integrate deliverables from multiple engineering teams into a cohesive and functional product.

  • Investigate, debug, and resolve (or delegate) system issues — ensuring ownership through resolution and fix verification.

  • Manage internal release cycles, working closely with stakeholders to define release content and stability requirements.

  • Operate hands-on in the lab: setup, bring-up, validation, and diagnostics of hardware/software systems.

  • Collaborate with production line teams, providing remote support and proactive monitoring of manufacturing processes.

  • Act as the first line of defense for production-related issues and drive root cause analysis and continuous improvements.

What we need to see:

  • B.Sc. in Electrical Engineering with software proficiency or B.Sc. in Computer Science with strong electrical understanding.

  • 5+ years of relevant experience

  • Strong lab and integration skills — hands-on experience working with real hardware is a must.

  • Solid understanding of embedded systems, system bring-up, and debugging tools (e.g., logic analyzers, oscilloscopes, JTAG).

  • Proven ability to take ownership and lead technical efforts across multidisciplinary teams.

  • Excellent communication and organizational skills — able to align multiple stakeholders.

Ways to stand out from the crowd:

  • Experience supporting products through transition from R&D to mass production.

  • Familiarity with release management tools, CI/CD pipelines, and version control systems.

  • Previous work with manufacturing environments or remote production monitoring.

  • Demonstrated ability to drive issues to closure in a fast-paced, multidisciplinary environment.

  • Passion for quality, efficiency, and continuous improvement.

NVIDIA has some of the most forward-thinking and hardworking people in the world working for us and, due to unprecedented growth, our world-class engineering teams are growing fast. If you're a creative and autonomous engineer with a real passion for technology, we want to hear from you. We are committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

Top Skills

Ci/Cd
Debugging Tools
Embedded Systems
Jtag
Logic Analyzers
Oscilloscopes
Software Development
Version Control Systems
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The Company
HQ: Santa Clara, CA
21,960 Employees
Year Founded: 1993

What We Do

NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”

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