Senior Silicon Reliability Engineer

NVIDIA Ltd.
Santa Clara, CA, United States
3 months ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
5 years minimum
Compensation
$168,000.0 - $264,500.0
Working hours
Regular working hours
Job source

Tech stack

Data Analysis Computer Graphics JMP (Statistical Software) Deep Learning Parallel Computation SAP Ariba

Job description

NVIDIA has continuously reinvented itself over three decades. Our 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. NVIDIA is a ā€œlearning machineā€ that constantly evolves by adapting to new opportunities which are hard to solve, which only we can pursue, and which matter to the world. This is our life’s work: to amplify human inventiveness and intelligence. Make the choice to join us today. We are seeking an outstanding candidate for Silicon Reliability Engineer who will serve as the foundry process reliability professional for NVIDIA in utilizing cutting edge technologies to deliver high performance products while ensuring world class reliability.

What you will be doing:

  • Work closely with wafer foundries and various functional groups such as Design, Advanced Technology Group and Operations to develop process reliability requirements for new technology nodes.

  • Drive foundries for NVIDIA’s test vehicle qualifications and process reliability improvement before taping out new products and support product bring-ups and quals.

  • Perform wearout reliability assessments and provide reliability guidance to design teams for Design for Reliability on the next generation products.

  • Provide reliability Vmax, aging guardband guidelines to product teams and develop reliability methodology to optimize product reliability performance.

  • Collaborate with foundries to plan and perform stress tests and build reliability models for extrinsic failure mechanisms.

  • Interact with foundries for excursion materials and assess a reliability risk for disposition.

Requirements

  • Deep understanding of cutting-edge semiconductor process technologies, reliability physics, and acceleration models.

  • Familiar with wearout failure mechanisms (TDDB, BTI, HCI, EM, SM) and models, wafer level reliability tests, test structures, and industry standards (JEDEC, AEC).

  • Proficient in reliability statistics, and hands on experience in failure rate calculations for early life, useful life and wearout period of reliability life curve.

  • Good knowledge of semiconductor process defects/root causes, failure analysis and defect screening methodologies.

  • Solid understanding in reliability degradation mechanisms in logic and memory circuits.

  • Hands on skills in reliability data analysis, particularly using statistical tools such as JMP.

  • Excellent written and verbal communication, and presentation skills.

  • PhD or MS in Electrical Engineering, Physics, or a related major (or equivalent experience).

  • 5+ years of overall experience in semiconductor reliability.

Benefits & conditions

With competitive salaries and a generous benefits package, we are widely considered to be one of the technology world’s most desirable employers; we have some of the most forward-thinking and hardworking people in the world working for us and, due to unparalleled growth, best-in-class teams are rapidly growing. If you’re creative and autonomous with a real passion for your work, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 168,000 USD - 264,500 USD.

You will also be eligible for equity and benefits (https://www.nvidia.com/en-us/benefits/) .

Apply for this position

This job is hosted externally. Click below to view the full posting and apply.

Apply on juju.com

Good distractions

Talks and stories from around this role — technically off-topic, practically not.

1:25 min

Distinguishing artificial intelligence from deep learning

Sam Witteveen Ā· Coffee With Developers

1:48 min

Automating exploratory data analysis within training pipelines

Dora Petrella Ā· WWC 2023

2:23 min

Highlighting safe technical careers in established hardware organizations

Rudi Bauer +2 Ā· Cappuccino with HR

2:17 min

Distinguishing between AI, machine learning, and deep learning

Mary Grygleski Mary Grygleski Ā· LIVE

1:36 min

Performing exploratory data analysis to uncover underlying patterns

Julian Joseph Ā· LIVE

40 sec

Hardware durability labs and robot testing methods

Chris Heilmann +1 Ā· LIVE

Videos

See all

Related articles

See all