Principal Engineer - Dashcam Engineering

As a Principal Engineer in Dashcam Engineering at Hivemapper, your primary responsibilities will include building embedded applications, developing sensor drivers, and creating sensor fusion pipelines. You'll also be accountable for overseeing the architecture of efficient userspace software systems that handle high-frequency, real-time data. Collaboration with hardware, manufacturing, and SoC software partners will be essential, as you strategize and execute tactical plans that bolster our operational efficiency. This role requires a hands-on approach to developing high-quality software that fully leverages our cutting-edge hardware capabilities.

To qualify for the Principal Engineer - Dashcam Engineering role at Hivemapper, candidates should have a solid understanding of embedded sensor platforms such as RGB, GNSS, and IMUs. Hands-on experience with Linux OS development for embedded systems is crucial. Proficiency in programming languages such as C or C++, along with familiarity in Python, Typescript, Node.Js, or Go, is also required. Additional experience with Advanced Driver Assistance Systems (ADAS), Mapping technologies, or ML frameworks like PyTorch or TensorFlow will be advantageous.

At Hivemapper, the work environment for a Principal Engineer in Dashcam Engineering is highly collaborative and technology-driven. You will be immersed in a culture of innovation, working alongside talented engineers, data scientists, and hardware experts. The dynamic nature of our projects means you will be routinely engaged in cross-functional teamwork, encouraging knowledge sharing and creative problem-solving. Our office in San Francisco supports an open and inclusive atmosphere, aligning with our commitment to diversity.

Hivemapper places a strong emphasis on professional development for members of its engineering team, including Principal Engineers in Dashcam Engineering. Opportunities for continuing education, participation in workshops, and attendance at industry conferences are actively encouraged. With a culture that recognizes the value of learning from failures and successes alike, you will find that Hivemapper fosters growth through mentorship programs and a supportive work environment that promotes knowledge transfer.

Employees, including those in the Principal Engineer position at Hivemapper, enjoy a comprehensive benefits package that includes medical, dental, and vision coverage, as well as family leave options and a 401(k) retirement plan. Wonderful perks such as unlimited Flex PTO, commuter benefits, and paid lunches create a fulfilling work-life balance, ensuring that our team members feel valued and well-equipped to perform their roles sustainably.

When answering this question, it's important to highlight specific projects where you've worked on embedded systems and how they relate to dashcam technologies. Discuss the types of sensors you have experience with, how you developed software for these systems, and any challenges you overcame during the process. Providing real-world examples can illustrate your problem-solving abilities and technical knowledge effectively.

In your response, discuss methodologies such as filtering, data integrity checks, and the algorithms you're familiar with utilizing in sensor fusion. You can mention your understanding of performance metrics, how you monitor latency, and your approach to improving accuracy. Be prepared to discuss specific cases where you've implemented these strategies successfully.

In a fast-paced environment like Hivemapper, prioritizing tasks effectively is crucial. Highlight your approach to project management, such as using prioritization frameworks (like the Eisenhower Matrix) or project management tools that help you track deadlines and dependencies. Discuss your experience collaborating with team members to align on priorities and ensure transparency in your processes.

Use this question to share an example where you collaborated with hardware engineers, emphasizing communication and coordination. Talk about any tools you used to document and share progress, and how you addressed any conflicts or differences in technical understanding. Illustrating your ability to work well in cross-disciplinary teams will demonstrate your leadership and teamwork skills.

Be specific about any projects where you've applied machine learning principles using these frameworks. Discuss your familiarity with model training, data preprocessing, and inference. If applicable, share any challenges you faced while implementing ML solutions in embedded systems, and how you resolved them through innovative approaches or collaboration with data scientists.

To answer this, share your coding practices such as conducting code reviews, the use of version control systems, and implementing unit tests and integration testing. Highlight methodologies like Agile or DevOps that you've used to ensure thorough testing and continuous deployment. Discuss how you gather user feedback to iterate on your code effectively.

Select a specific example that shows your technical depth and problem-solving abilities. Describe the context, the challenges you faced, and the steps you took to address the issue. Highlight any tools or frameworks you utilized and the lessons learned that you can apply in future projects.

Share your methods for staying informed, such as following industry publications, attending conferences, and participating in relevant online forums or communities. You might also mention personal projects, online courses, or webinars that you engage in to continuously develop your expertise in embedded systems.

Express your enthusiasm for Hivemapper's mission and technology, linking your personal values to the company's objectives. Discuss specific aspects of Hivemapper's work and culture that resonate with you, such as the emphasis on innovation in mapping technologies and the collaborative work environment. This shows that you’ve researched the company and understand its goals.

When answering this question, discuss factors such as data integrity, real-time processing capabilities, and efficient resource utilization. Emphasize your understanding of trade-offs involved in design decisions, such as balancing accuracy with performance, and how you approach ensuring scalability as data volumes increase.