Software Engineer - Sensors

As a Software Engineer - Sensors at Applied Intuition, your main responsibilities include developing and maintaining sensor drivers, ensuring the effective integration of sensors with various autonomy subsystems, and debugging sensor performance for real-world applications. You'll work collaboratively with teams across hardware, perception, and planning to optimize sensor functionality and build tools for in-depth analysis and visualization.

To qualify for the Software Engineer - Sensors position at Applied Intuition, a strong proficiency in C++ is essential, along with experience in developing low-level software and writing drivers for automotive sensors like lidar, radar, or cameras. A solid understanding of Linux, networking, and real-time data processing is also crucial, as well as strong debugging skills related to hardware-software interactions.

While not mandatory, preferred skills for the role of Software Engineer - Sensors at Applied Intuition include experience with real-time systems, familiarity with sensor stacks in automotive or robotics, and hands-on experience in optimizing sensor performance. Knowledge in signal processing, sensor fusion, or embedded systems is also beneficial, alongside proficiency in Python for scripting and data analysis.

At Applied Intuition, the work environment for a Software Engineer - Sensors is collaborative and innovative. We predominantly operate from our Mountain View office, valuing in-person collaboration five days a week, though we support flexible working arrangements that allow for occasional remote work or adjusted schedules to accommodate personal commitments.

You should consider applying for the Software Engineer - Sensors position at Applied Intuition if you're looking to be part of a forward-thinking team that is paving the way in the autonomous vehicle industry. With a focus on flexibility, collaboration, and professional growth, this role offers the chance to work with cutting-edge technology while developing skills that will be foundational to the future of intelligent machines.

When answering this question, focus on specific projects where you've developed or maintained sensor drivers. Highlight the types of sensors you've worked with, the programming languages used, and any relevant challenges you've faced, along with how you overcame them.

Discuss your methods for testing and validating sensor data, such as implementing real-world scenario tests, collaborating with hardware teams to confirm functionality, and employing various debugging techniques to enhance performance accuracy.

Detail your hands-on experience with real-time systems, including any specific technologies or frameworks you've used. Emphasize how you managed latency issues and maintained system performance under varying data loads.

Provide a clear example of a challenging debug experience, explaining your approach to identifying the issue, the tools you used, and how your actions contributed to a resolution while ensuring product quality.

Talk about past experiences collaborating with teams from different engineering disciplines, emphasizing communication skills, adaptability, and any strategies that proved effective in ensuring alignment and productivity.

Discuss a variety of performance analysis tools you are familiar with, such as profilers or debuggers, and give examples of how you’ve used these tools to improve sensor or system performance in previous projects.

Describe specific design patterns you've employed in sensor integration projects, such as Observer or Strategy patterns, and explain how they improved code organization and system performance.

Share your strategies for task prioritization, perhaps using tools like Kanban or Agile methodologies, and provide examples of how you’ve effectively managed competing deadlines or project demands.

Discuss some of the inherent challenges such as dealing with sensor noise, ensuring compatibility across different sensors, and real-time data processing demands, while also offering potential solutions you might implement.

Emphasize your commitment to ongoing learning through industry blogs, academic papers, online courses, or professional networks. Mention any specific resources you regularly utilize to enhance your knowledge in the field.