Software Engineer, Localization

As a Software Engineer, Localization at Agtonomy, your primary responsibilities will include developing state estimation algorithms to enhance the precision of our tractor systems. You will work on implementing features to improve both local and global pose estimates while designing mitigation strategies for pose estimation systems. Additionally, you’ll create robust online and offline calibration routines and collaborate with interdisciplinary teams to advance our localization technology.

Candidates for the Software Engineer, Localization role at Agtonomy should ideally possess over 5 years of experience in software development within autonomy or robotics fields. A strong background in deploying state estimation algorithms and deep knowledge of sensing technologies such as LiDAR and camera systems is essential. Preferred qualifications include an MS or PhD in Robotics or related fields and proficiency in modern C++.

A candidate is deemed a strong fit for Agtonomy's Software Engineer, Localization position if they have hands-on experience architecting state estimation systems and are familiar with the intricacies of filter design and pose graph optimization. Additionally, a passion for sustainable agriculture and familiarity with advanced visual and lidar odometry algorithms would be significant assets.

At Agtonomy, the salary range for the Software Engineer, Localization position is between $160,000 and $220,000 per year, depending on factors like experience and educational background. Furthermore, this salary package includes equity options, comprehensive benefits, and unlimited PTO, providing an attractive opportunity for the right candidate.

Agtonomy fosters a collaborative work environment that supports professional growth for a Software Engineer, Localization through exposure to real-world challenges in agricultural tech. The company offers benefits like a flexible spending account, 401k plans, and 100% covered medical, dental, and vision insurance to ensure employees maintain a healthy work-life balance while contributing to impactful initiatives.

Certainly! Kalman filters are algorithms that use a series of measurements observed over time, containing statistical noise and other inaccuracies, to produce estimates of unknown variables. For a Software Engineer, Localization at Agtonomy, understanding how to implement Kalman filters is crucial, as they directly improve the accuracy of pose estimates in our agricultural robots.

Integrating diverse sensing technologies requires a deep understanding of each sensor's capabilities and limitations. As a candidate for the Software Engineer, Localization role, you'd need to explain that fusing data from LiDAR, cameras, and IMUs involves understanding the design tradeoffs and potential conflicts between the inputs, ensuring robust and accurate final outputs.

To mitigate failures in pose estimation, strategies may include creating fallback systems that can activate when primary systems fail. You could also discuss deploying robust sensor calibration routines and maintaining real-time monitoring to quickly address any discrepancies, ensuring the safety and reliability of our agricultural machinery.

In your response, focus on specific projects where you utilized modern C++ for developing efficient algorithms tailored for resource-constrained environments. Highlight how you've optimized code for performance and maintainability, ensuring high standards of quality while contributing to the success of robotics initiatives.

Validating algorithms in real-world situations involves thorough field testing at customer farms, where you would deploy the algorithms on actual vehicles. Discuss how you monitor performance metrics and iterate on your solutions based on feedback and observed results to ensure optimal performance in the agricultural setting.

Working collaboratively is vital, especially in a cross-disciplinary role like Software Engineer, Localization. You should emphasize the importance of communication, sharing insights, and integrating diverse perspectives from different team members to enhance the overall project outcomes, particularly in complex areas like robotics.

Discuss the various trade-offs, such as accuracy versus complexity, cost, and reliability. Explain how each sensing modality contributes differently to the overall estimation process and how making informed decisions about sensor integration can enhance performance while managing resource constraints.

Talk about the complexities of varying terrain and environmental conditions that can impact sensor readings and algorithm performance. Highlight the need for adaptable algorithms and robust systems that can function effectively across diverse scenarios encountered in agriculture.

Mention your commitment to continuous learning through attending conferences, participating in workshops, actively engaging in relevant online communities, and reading current research papers. This illustrates your proactive approach toward staying informed about the latest innovations and technologies in robotics.

Share your personal connection or passion for sustainable practices and how they align with Agtonomy's mission. Discuss how you find fulfillment in applying your technical skills to create solutions that contribute positively to environmental challenges in agriculture.