AI-Assisted Memory Design Intern

Posted 9 Hours Ago
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Singapore, SGP
In-Office
Internship
Hardware • Internet of Things • Software • Wearables • Semiconductor
Join the Team Powering the Future of Edge AI
The Role
Explore AI/ML-assisted and self-adaptive memory design techniques for optimizing performance, power efficiency, robustness, and yield. Analyze memory assist methods, operating conditions, failure signatures, and process variations. Develop adaptive monitoring, prediction, voltage, timing, and read/write control techniques, then document, benchmark, and present the proposed architecture and design insights.
Summary Generated by Built In

Company Overview

Ambiq is on a mission to enable intelligence everywhere — powering the AI edge revolution with the world's lowest-power semiconductor solutions.

Built on our proprietary sub- and near-threshold technology, our chips deliver multi-fold improvements in energy efficiency without costly process scaling. Since 2010, we've shipped over 300 million units to customers building smarter wearables, medical devices, IoT products, and AI-powered edge applications.

Our cross-functional teams span design, research, development, production, marketing, sales, and operations across Austin, Hsinchu, Shanghai, Shenzhen, and Singapore. We move fast, tackle hard problems, and create space for people to grow through complex, meaningful work that shapes the future of technology.

We're looking for self-motivated, creative problem-solvers who are eager to push technological limits and make a real impact in energy efficiency.

At Ambiq, we live by five values: Innovate. Collaborate. Focus. Learn. Achieve.

If that's you, join us — the intelligence everywhere revolution starts here.


About the Role

Work closely with foundation IP team and explore Artificial Intelligence (AI)-assisted and self-adaptive memory design techniques that leverage operating-condition awareness to dynamically optimize memory operation for improved performance, power efficiency, and yield.

Responsibilities
  • Analyze conventional memory assist techniques and explore how AI/ML-driven learning and optimization can enhance control logic, supply modulation, boosted/negative voltages, and replica/tracking schemes.
  • Investigate AI/ML-based techniques to analyze memory operating conditions, identify failure signatures, and predict robustness across voltage, temperature, process variation, timing, and parasitic effects.
  • Explore self-timed, self-adaptive, and intelligent memory techniques that learn from operating conditions and dynamically optimize assist decisions for improved robustness, power, performance, and yield.
  • Develop AI/ML-assisted monitoring, prediction, and adaptive control techniques for dynamically optimizing memory assist, voltage, timing, and read/write operations.
  • Document, benchmark, and present the proposed self-adaptive memory architecture and key design insights.
Qualifications
  • Pursuing a BS in EE, CE, Microelectronics, or related field (rising Junior/Senior).
  • Coursework in Digital Integrated Circuits, Analog Circuit Design, VLSI Design, or Semiconductor Devices.
  • Coursework in Machine Learning, Optimization, Data Analytics, Computational Methods or related courses.
  • Basic understanding of CMOS circuit design and IC layout (DRC/LVS) concepts.
  • Working knowledge of Python and basic ML concepts (regression, clustering).
  • Comfort working with large datasets.
  • Strong analytical and problem-solving ability.
Nice to Have
  • Exposure to layout/schematic tools (Virtuoso, Calibre) or characterization tools (SiliconSmart, PrimeTime, Liberate)
  • Interest in low-power IP for edge AI/IoT
  • Strong passion, eagerness, and curiosity to learn and explore transistor-level circuit design.

Skills Required

  • Pursuing a BS in Electrical Engineering, Computer Engineering, Microelectronics, or a related field as a rising junior or senior
  • Coursework in Digital Integrated Circuits, Analog Circuit Design, VLSI Design, or Semiconductor Devices
  • Coursework in Machine Learning, Optimization, Data Analytics, Computational Methods, or related subjects
  • Basic understanding of CMOS circuit design and IC layout, including DRC/LVS concepts
  • Working knowledge of Python and basic machine learning concepts, including regression and clustering
  • Comfort working with large datasets
  • Strong analytical and problem-solving ability
  • Exposure to layout or schematic tools such as Virtuoso and Calibre
  • Exposure to characterization tools such as SiliconSmart, PrimeTime, or Liberate
  • Interest in low-power IP for edge AI and IoT
  • Passion and eagerness to learn transistor-level circuit design
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The Company
HQ: Austin, Texas
220 Employees
Year Founded: 2010

What We Do

Ambiq® enables intelligence (AI and beyond) everywhere by delivering the lowest-power semiconductor solutions for battery-powered edge devices. As a pioneer in ultra-low power SoCs, Ambiq empowers wearables, IoT, smart home, healthcare, and industrial products with always-on, energy-efficient intelligence. Backed by our SPOT® technology and global innovation leadership, Ambiq is shaping the future of edge AI.

Why Work With Us

Direct influence on silicon architecture Smaller, high-impact teams Hardware/software co-design in real time Faster technical decision cycles Visible ownership at Staff & Director level Austin-based collaboration culture

Ambiq Offices

OnSite Workspace

Building silicon and embedded AI systems requires tight collaboration across firmware, hardware, validation, and architecture teams. We believe the hardest engineering problems are solved through direct, daily collaboration.

Typical time on-site:
HQAustin, Texas
Taiwan
China
Singapore
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