Senior Implementation Methodology Engineer

Reposted 9 Days Ago
Be an Early Applicant
2 Locations
In-Office
168K-311K Annually
Senior level
Artificial Intelligence • Computer Vision • Hardware • Robotics • Metaverse
The Role
Responsible for front-end design implementation methodologies, flow automation, and application support to optimize power, performance, and area on critical designs.
Summary Generated by Built In

NVIDIA has continuously reinvented itself over two decades. Our invention of the GPU in 1999 fueled 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 “learning machine” that constantly evolves by adapting to new opportunities that are hard to pursue, that only we can tackle, and that matter to the world. This is our life’s work, to amplify human creativity and intelligence. Make the choice to join us today. 

What you’ll be doing:

  • You will be responsible for all aspects of front-end design implementation methodologies (synthesis, formal-equivalence-checking), flow automation and application support.

  • Use NVIDIA implementation flows and EDA tool expertise to improve power, performance and area on NVIDIA's most critical designs

  • You will collaborate with logic designers, physical designers and EDA vendors to solve exciting implementation issues and develop new solutions. 

  • Provide support for EDA tools and flows

What we need to see:

  • BS or MS in Electrical Engineering, Computer Engineering, or related fields (or equivalent experience).

  • 8+ years of experience in logic design implementation and/or physical design implementation

  • Deep understanding of logic optimization techniques and relative area, timing, and power trade-offs

  • Strong understanding of physical design implementation eg: physical synthesis, placement, routing, logic restructuring, etc.

  • Should be a power user of synthesis and/or place and route EDA tools from Synopsys (DC/FC), Cadence (Genus/Innovus)

  • Good debugging and problem-solving skills

  • Strong interpersonal skills along with the ability to work in a dynamic team

Ways to stand out from the crowd:

  • Prior experience in physical implementation

  • Proficiency in Perl, Python, Tcl, Make scripting

NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most experienced and dedicated people in the world working for us. Are you creative and autonomous? Do you love the challenge of constant innovation and creating the highest performance products in the industry? If so, we want to hear from you. Come, join NVIDIA VLSI team and help build the real-time, cost-effective computing platform driving our success across multiple fields such as Deep Learning and AI, Robotics and Autonomous Driving, Gaming and High Performance Computing. 

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 for Level 4, and 196,000 USD - 310,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until January 13, 2026.

This posting is for an existing vacancy. 

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is 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

Cadence
Eda Tools
Make
Perl
Python
Synopsys
Tcl
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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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