Senior Machine Learning Engineer

As a Senior Machine Learning Engineer at Axon, your key responsibilities include developing and optimizing algorithms for detecting, tracking, and classifying flying objects using advanced camera systems. You’ll innovate methodologies for search and tracking in a networked camera system, collaborate with the simulation team for algorithm testing, and deploy models onto edge computing devices using Python and C++. Moreover, you’ll engage with the latest industry advancements to continuously improve your contributions to airspace security.

To qualify for the Senior Machine Learning Engineer position at Axon, candidates should have at least 5 years of experience with Python, expertise in object-oriented design methods, and familiarity with tracking algorithms like Kalman and particle filters. Proficiency in computer vision and detection algorithms is essential, alongside a solid background in computer science. Knowledge of reinforcement learning and a strong problem-solving mindset will also benefit applicants.

In the Senior Machine Learning Engineer position at Axon, Python and C++ are crucial programming languages. Python is primarily used for developing and testing machine learning algorithms, while C++ is important for deploying these algorithms onto edge computing devices. Familiarity with these languages ensures effective implementation of innovative solutions for airspace security.

Axon actively promotes learning and development for Senior Machine Learning Engineers, providing access to various training programs, conferences, and workshops. This support allows engineers to stay current with industry advancements, refine their skills, and contribute more effectively to the company's mission. The collaborative environment also encourages knowledge sharing within the talented team.

Axon offers a range of competitive benefits for Senior Machine Learning Engineers, including a salary with a 401k employer match, discretionary paid time off, and paid parental leave. Employees also enjoy comprehensive medical, dental, and vision plans, wellness support, fitness programs, and continuous learning opportunities. This commitment to employee well-being reflects Axon’s dedication to creating a supportive workplace.

Discuss a specific project where you developed algorithms for tracking aerial objects. Highlight the challenges you faced and how you implemented innovative solutions. Be sure to mention the technologies and methodologies you employed, such as camera systems and tracking algorithms, and the impact of your work on improving airspace security.

When optimizing machine learning algorithms, I start by assessing the data and defining performance metrics. I utilize techniques such as hyperparameter tuning, feature selection, and model evaluation to iteratively refine the algorithm. Sharing specific examples of how you improved the accuracy or efficiency of an algorithm in your previous work can showcase your expertise.

In my previous role, I developed and deployed machine learning models on edge devices by ensuring efficiency and scalability. I utilized programming languages like Python and C++ and focused on optimizing the algorithms to fit within the constraints of edge computing resources. Providing a tangible outcome from this experience can strengthen your answer.

I maintain my knowledge of industry trends by attending conferences, participating in online courses, reading research papers, and collaborating with fellow professionals in the field. I believe continuous learning is vital to effective contributions in a rapidly evolving domain like machine learning, especially in applications such as airspace security.

I have worked with algorithms such as Kalman filters and particle filters for tracking moving objects in video data. In my previous projects, I focused on adapting these algorithms to handle various scenarios involving aerial threats, explaining how each algorithm improved the model's tracking accuracy and performance.

I faced a significant challenge while developing an algorithm to detect drones in cluttered environments. By employing data augmentation techniques and adjusting the model architecture, I improved detection rates. Sharing this experience will demonstrate your problem-solving skills and ability to overcome adversity in your work.

I focus on modular and flexible model development approaches, applying optimization techniques that enhance performance without compromising on scalability. Discussing specific strategies, like employing efficient data handling or adopting batch processing methods, can illustrate your understanding of creating scalable solutions.

My preferred tools include TensorFlow and PyTorch due to their robust ecosystems and support for deep learning applications. I appreciate these libraries for their flexibility and efficiency, enabling me to quickly test and iterate on various models. Mentioning specific projects where you effectively utilized these tools can add depth to your answer.

I view feedback as an essential component of my professional growth. I actively seek constructive criticism, implementing suggestions to improve my work. Sharing a specific instance where you successfully integrated feedback into your project can convey your adaptability and willingness to learn.

I am drawn to the mission of Axon to protect life and enhance airspace security. I appreciate the opportunity to work with advanced technology in a collaborative environment, furthering my skills while making a meaningful impact. Articulating your alignment with Axon's mission will reflect your enthusiasm for the role.