Internship

NVIDIA 2025 Internships: Mixed Signal / Circuit Design

Confirmed live in the last 24 hours

NVIDIA

NVIDIA

10,001+ employees

Designs GPUs and AI computing solutions

Automotive & Transportation
Enterprise Software
AI & Machine Learning
Gaming

Compensation Overview

$18 - $71Hourly

Santa Clara, CA, USA

Category
Electronics Design Engineering
Embedded Systems Engineering
Electrical Engineering
Required Skills
Verilog
Python
Perl
VHDL
C/C++
Requirements
  • Currently pursuing a Bachelor’s, Master’s, or Ph.D. in Electrical Engineering or Computer Engineering, with a focus on Analog/Mixed Signal Circuit Design.
  • Solid understanding of analog fundamentals.
  • Knowledge of analog simulation for noise analysis, loop stability analysis, ac/dc/tran analysis, Monte-Carlo, etc.
  • Basic understanding of layout and physical design considerations.
  • Experience in CMOS analog circuit design (especially in FINFET).
  • Working knowledge of high-speed circuit design, serial communication standards, PLL, DAC, ADC, and bandgaps.
  • Familiarity with lab testing equipment (e.g., oscilloscopes, spectrum analyzers).
  • You are pursuing a MS/PHD in Electrical or Computer Engineering.
  • Good understanding of digital circuit design and device fundamentals.
  • Experience with Place and Route design tools and datapath Tiling techniques.
  • Practical experience with Spice simulations.
  • Familiarity with automation methods/algorithm and power distribution and power management circuits/solutions.
  • Verilog, SystemVerilog, VHDL, Perl, TCL, C, C++, Python.
  • Standard EDA tools, such as Cadence, Synopsys, etc.
  • SPICE simulations.
Responsibilities
  • Work on projects that have a measurable impact on our business.

NVIDIA designs and manufactures graphics processing units (GPUs) and system on a chip units (SoCs) for various markets, including gaming, professional visualization, data centers, and automotive. Their products, such as GPUs, are essential for high-performance computing and artificial intelligence applications. NVIDIA's GPUs work by processing large amounts of data simultaneously, making them ideal for tasks like gaming graphics and complex computations in AI and machine learning. Unlike many competitors, NVIDIA focuses on both hardware and software solutions, offering cloud-based services that enhance the capabilities of their products. The company's goal is to drive innovation in AI and HPC, providing advanced solutions that cater to a wide range of clients, from gamers to researchers and enterprises.

Company Stage

IPO

Total Funding

$19.5M

Headquarters

Santa Clara, California

Founded

1993

Growth & Insights
Headcount

6 month growth

13%

1 year growth

25%

2 year growth

42%
Simplify Jobs

Simplify's Take

What believers are saying

  • NVIDIA's diverse investment portfolio offers employees exposure to cutting-edge technologies and industries, fostering a dynamic and innovative work environment.
  • The company's strategic acquisitions and partnerships provide opportunities for professional growth and cross-industry collaboration.
  • NVIDIA's strong financial backing and market leadership ensure stability and resources for continued innovation and development.

What critics are saying

  • NVIDIA's expansion into various sectors may dilute its focus and resources, potentially impacting its core GPU business.
  • The competitive landscape in AI, robotics, and autonomous vehicles is intense, posing challenges for NVIDIA to maintain its leadership position.

What makes NVIDIA unique

  • NVIDIA's strategic investments in diverse sectors like robotics, AI disease modeling, and driverless trucks highlight its commitment to pioneering technologies beyond its core GPU business.
  • The acquisition of Shoreline.io, a software startup, indicates NVIDIA's focus on enhancing its software capabilities, setting it apart from competitors primarily focused on hardware.
  • NVIDIA's involvement in significant funding rounds for companies like Mistral AI and Bright Machines showcases its influence and leadership in the AI and intelligent manufacturing sectors.

Help us improve and share your feedback! Did you find this helpful?