Senior Machine Learning Engineer II
Axon Enterprise - Seattle, Washington, United States
Posted Feb 2, 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
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- Salary
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- 401(k) match
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Schedule
- Shift type
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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
Senior Machine Learning Engineer II Seattle, Washington, United States Join Axon and be a Force for Good. At Axon, we're on a mission to Protect Life. We're explorers, pursuing society's most critical safety and justice issues with our ecosystem of devices and cloud software. Like our products, we work better together. We connect with candor and care, seeking out diverse perspectives from our customers, communities and each other. Life at Axon is fast-paced, challenging and meaningful. Here, you'll take ownership and drive real change. Constantly grow as you work hard for a mission that matters at a company where you matter. Your Impact Are you passionate about AI? Do you love engineering solutions enabling fast, cutting-edge science? Are you eager to contribute to building products impacting the world for the greater good? As a ML Engineer at Axon, you will contribute to architecting and implementing the platform used by Axon scientists to bring safe, secure, reliable AI features to Axon devices. Collaborating closely with scientists and device engineers, you will enable new AI capabilities for Axon products (Fleet, Axon Body, Axon Air, and more). You will also make sure As part of a multidisciplinary team, you will be exposed to a wide range of domains and applications: from Automatic License Plates Recognition to Object Tracking and Automatic Speech Recognition. The ideal candidate will have a consistent track record of successfully optimizing models for the edge and deploying AI models at the edge, including on device with limited memory & connectivity,
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