Principal Machine Learning Engineer

Axon•Seattle, WA

7d•Hybrid

About The Position

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 Principal ML Engineer at Dedrone, you will contribute to architecting and implementing the platform used by Dedrone scientists to bring safe, secure, reliable AI features to the field of counter UAS devices. Collaborating closely with scientists and software engineers, you will enable new AI capabilities for Dedrone products. As part of a multidisciplinary team, you will be exposed to a wide range of domains and applications: from Multi-modal Sensor Fusion to Real Time Systems with distributed edge compute. 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, to serve users world wide. They will understand the AI lifecycle and be capable of supporting scientists end-to-end along the development of new machine learning models and AI capabilities, leading to better and more responsible AI solutions. They are willing to be bold and stand up to support the team in enabling drone safety measures across Public Safety, Enterprise and Defense domains.

Requirements

Bachelor’s Degree in Computer Science, Engineering, Physics, Mathematics or an equivalent highly technical field.
13+ years of software engineering experience and a proven track record of successfully architecting and maintaining large-scale distributed platforms.
Experience with AI on chips, on device model deployment and management.
Proficiency in python, C++, familiarity with ML frameworks such as TensorFlow, or PyTorch.
Advanced knowledge and hands-on experience with on chips development.
Excellent problem solving skills and ability to dive into system architecture, design, performance metrics, code, test plans, project plans, deployments and operations
Comfort communicating and interacting with scientists, engineers and product managers.

Nice To Haves

Master’s Degree or PhD in Computer Science, Engineering, Physics, Mathematics or an equivalent highly technical field.
Hands-on experience in solving Computer Vision or Natural Language Understanding problems in a business setting.
Familiarity with responsible AI, de-biasing, model encryption and de-identification techniques.

Responsibilities

Architect and develop secure, privacy-preserving, on device solutions to enable the continuous improvement of existing AI models.
Collaborate with scientists in architecting and implementing state-of-the-art edge distributed training techniques.
Implement on device monitoring solutions used for continuous model improvement
Implement innovative model compression solutions to enable AI at the edge.
Impact the team by bringing your own expertise and deep knowledge of the state-of-the-art to introduce new techniques leading to tangible impact in terms of model fairness, performance, and platform scalability.