RF Machine Learning Engineer II

As an RF Machine Learning Engineer II at Axon, your main responsibilities include developing software-defined radio systems for wireless protocol detection and classification, applying machine learning to enhance DSP challenges, and implementing advanced signal processing techniques. You'll also conduct experiments to ensure the robustness of your solutions while collaborating with cross-functional teams to integrate these advanced systems.

To apply for the RF Machine Learning Engineer II position at Axon, candidates should have a minimum of six years of experience in software-defined radio development, a deep understanding of digital signal processing, and proficiency in wireless communication protocols. Familiarity with machine learning frameworks like TensorFlow, PyTorch, or JAX is also essential, along with a demonstrated ability to implement sophisticated DSP methods.

Important skills for the RF Machine Learning Engineer II role at Axon include a strong grasp of linear algebra, statistics, and optimization methods. Candidates should be comfortable with advanced signal processing techniques, such as cyclostationary analysis and beamforming, as well as experience handling large datasets and developing efficient data pipelines using GPU-accelerated computing.

An RF Machine Learning Engineer II at Axon directly contributes to our mission to protect life by designing and deploying systems that enhance communication safety and efficiency. By leveraging robust machine learning and DSP solutions, you will help develop technologies that improve real-world safety outcomes, ensuring that our systems are prepared for any challenges encountered in dynamic environments.

At Axon, RF Machine Learning Engineers II can expect competitive salaries along with 401k plans with employer matching, paid parental leave, medical, dental, and vision insurance, and wellness programs. Additionally, Axon promotes personal growth through learning and development opportunities while fostering a supportive and inclusive work environment.

When discussing your experience with SDR, be specific about the projects you’ve worked on, the technologies you used, and how you were able to overcome challenges. Highlight instances where your work in SDR contributed to successful outcomes in communication systems, such as protocol analysis or real-time fluctuations.

Share your proficiency in frameworks like TensorFlow or PyTorch with specific examples of how you've utilized them for machine learning integration in DSP applications. Discuss the types of models you developed or optimized and the real-world problems they addressed, ideally through quantitative metrics showcasing your success.

In your response, describe concrete examples of DSP techniques you’ve implemented, such as beamforming or multipath mitigation. Explain the context of their application and the end results—this can relate to enhanced signal detection or improved wireless communication quality.

Discuss your strategy for managing large datasets, such as data cleaning, preprocessing, and ensuring efficient storage. Emphasize the tools you utilize, like Python libraries or specific pipelines, and any performance improvements that resulted from your approach.

Explain the methods and algorithms you’ve used to extract features from spectral data. Discuss how these features informed your analysis and contributed to the decision-making process in project outcomes, providing specific examples of how they were applied successfully.

Outline a specific issue you encountered in RF communications, detailing the complexities involved. Describe the steps you took to diagnose and resolve it, and highlight the positive impact your solution had on the project or the overall system.

In answering this, talk about your approach to validating models, including testing under various conditions and refining them based on performance data. Highlight methodologies for stress-testing your systems and any resulting metrics that demonstrate reliability.

Detail your experience collaborating with diverse teams, mentioning specific roles you interacted with—like product managers or hardware engineers. Illustrate how collective efforts led to improved project outcomes and fostered an innovative environment.

Share your passion for RF communications and machine learning, explaining what excites you about leveraging these technologies to solve complex problems. Consider tying your motivation to Axon's mission and how you envision contributing to a safer world.

Discuss your strategies for continuous learning, such as attending industry conferences, taking online courses, and engaging with communities in your field. Highlight specific resources you’ve found valuable and how they’ve influenced your work.