Sr. Staff Edge AI Applied Machine Learning Engineer
Ambiq Micro INC - Austin, Texas, United States
Posted Feb 3, 2026
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- Verification
- Not verified last checked Jun 13, 2026
- Salary
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- 401(k) match
- Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.
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Schedule
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- Weekend work
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Application
- Cover letter
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- Assessment
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- Deadline
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Where they hire
State eligibility is not yet verified.
About this role
Sr. Staff Edge AI Applied Machine Learning Engineer Austin, Texas, United States 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 290 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. Scope Ambiq is seeking an experienced Edge AI Applied ML Engineer with deep experience in audio and computer vision. In this role, you will design, train, optimize, and deploy highly efficient on-device AI models-from ultra-small (tens of KB) to larger (hundreds of MB) footprints-targeting resource-constrained, real-time, battery-powered devices. While the cloud has been the default home for AI, the next frontier is distributing intelligence everywhere-directly onto real-world devices. Edge AI enables real-time responsiveness, stronger privacy, lower bandwidth cost, and reliable operation even without connectivity. This role will
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