Ambiq

Ultra-low power MCUs for IoT wearables

AI-Assisted Memory Design Intern

Silicon & ASIC DesignConfirmed live in the last 24 hours

InternshipSingaporeNo salary listedIn-person

About the Job

Requirements

Currently pursuing a Bachelor of Science in Electrical Engineering, Computer Engineering, Microelectronics, or a related field; rising juniors or seniors are eligible.
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 concepts, including DRC/LVS.
Working knowledge of Python and basic machine learning concepts such as regression and clustering.
Comfort working with large datasets.
Strong analytical and problem-solving ability.

Responsibilities

Analyze conventional memory assist techniques and explore how artificial intelligence and machine learning-driven learning and optimization can enhance control logic, supply modulation, boosted and negative voltages, and replica and tracking schemes.
Investigate artificial intelligence and machine learning-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 artificial intelligence and machine learning-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.

Desired Qualifications

Exposure to layout or schematic tools such as Virtuoso and Calibre, or characterization tools such as SiliconSmart, PrimeTime, and Liberate.
Interest in low-power intellectual property for edge artificial intelligence and Internet of Things.
Passion, eagerness, and curiosity to learn and explore transistor-level circuit design.

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.

About the Company

Ambiq

Ultra-low power MCUs for IoT wearables

Ambiq designs and sells microcontroller units (MCUs) and system-on-chips (SoCs) that minimize power use for battery-powered IoT and wearable devices. Their chips achieve ultra-low power by combining energy-efficient hardware and software, allowing devices to run longer between charges. They differentiate themselves through a dedicated focus on low-power performance and by earning revenue from silicon sales, technology licensing, and partnerships. Their goal is to enable longer-lasting, energy-efficient endpoint devices across gaming, hearables, industrial IoT, smart homes, wearables, and related markets.

Company size: 201-500
Company stage: IPO
Headquarters: Austin, TX
Founded: 2010

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