Senior Autonomy Engineer - Motion Prediction

As a Senior Autonomy Engineer - Motion Prediction at May Mobility, you'll be responsible for developing and testing cutting-edge robotics software, collaborating with cross-functional teams to establish software and system requirements, and ensuring code quality through design and code reviews. Your role will also involve applying machine learning techniques to enhance our autonomous vehicles’ behavior and performance in real-time systems.

To qualify for the Senior Autonomy Engineer - Motion Prediction role at May Mobility, candidates should have a Bachelor's degree in Robotics, Computer Science, or a related field, along with over 5 years of real-world experience in robotics. Proficiency in C/C++ programming and experience in machine learning frameworks like PyTorch or TensorFlow are critical. A strong background in sensor processing and behavior development is also essential.

To make your application stand out for the Senior Autonomy Engineer - Motion Prediction position at May Mobility, highlight your experience with real-world robotic systems, particularly in areas such as intent prediction, planning, and optimization. Discuss specific projects where you successfully deployed machine learning models in real-time environments, and emphasize any mentoring experience you have with junior engineers.

As a Senior Autonomy Engineer - Motion Prediction at May Mobility, strong programming skills in C and C++ are essential. Additionally, proficiency in Python is beneficial, given its application in machine learning through frameworks like PyTorch and TensorFlow. Familiarity with software development tools is also a plus.

At May Mobility, a Senior Autonomy Engineer - Motion Prediction will have ample opportunity for career growth. The role involves mentoring junior engineers, which can lead to leadership positions. Additionally, as the company continues to innovate in the field of autonomous vehicles, there will be opportunities to work on new projects that enhance your expertise and advance your career.

When responding to this question, provide a specific example, detailing your technical approach and the obstacles you faced. Highlight how your contributions helped achieve successful outcomes in motion prediction within autonomous systems, and reflect on lessons learned that could benefit future projects.

To assure the quality of robotics software, discuss your commitment to code standards, implementation of design reviews, and rigorous testing procedures. Mention tools you've used, such as Git for version control, and how analyses like code reviews help enhance software reliability in real-time systems.

Provide insights into your experience by discussing the entire machine learning lifecycle, from training to deployment. Explain specific challenges you've faced and how you optimized models for performance, accuracy, and latency in real-world applications, particularly in robotics or AV contexts.

For this question, focus on specific examples of how you've collaborated with teams from diverse backgrounds, such as hardware engineers or data scientists, to drive project success. Highlight communication strategies you employed to foster teamwork and coordinate efforts effectively.

Specify your experience with frameworks like PyTorch or TensorFlow, discussing projects where you implemented them successfully. Describe your familiarity with their features and any significant contributions you made while using these frameworks in your previous roles.

Discuss your approach to failure, emphasizing a growth mindset. Highlight how you analyze failed results and iterate on your work to achieve success. Provide an example of a failed project and the subsequent steps you took to improve future outcomes.

Detail specific data balancing techniques you've implemented, such as oversampling, undersampling, or synthetic data generation. Explain why data balancing is crucial in enhancing model performance and how you've utilized these techniques successfully in previous projects.

Begin by explaining that sensor fusion combines data from multiple sensors to produce more reliable and accurate information. Discuss specific algorithms or methods you’ve employed to implement sensor fusion in robotics projects and the benefits it brings to autonomous systems.

Discuss your practices for continuous learning, such as attending industry conferences, reading research papers, participating in online courses, or being active in robotics forums. Mention key publications or thought leaders in the space that you follow to keep up with new trends and technologies.

Describe a structured approach you take in mentoring junior engineers, which may include setting clear objectives, providing regular feedback, and facilitating hands-on experience. Share examples where mentoring positively influenced a junior engineer's skill set or career development.