Staff Software Engineer, Perception

As a Staff Software Engineer, Perception at AeroVect, your main responsibilities include deploying 3D object detection models with techniques that utilize various modalities such as camera, LiDAR, and radar. You'll also be building a robust data collection and evaluation pipeline to systematically assess model performance. Another key task is developing exclusive datasets for model training and evaluation, all while training and analyzing state-of-the-art models to ensure the highest level of accuracy and efficiency in our innovative systems.

Candidates for the Staff Software Engineer, Perception role at AeroVect typically require a Bachelor’s or Master’s degree in Computer Science, Robotics, Deep Learning, or a similar field. Additionally, you should possess over 5 years of focused experience in deploying 2D and 3D object detection models, along with strong proficiency in Python. Having a PhD in a relevant discipline and a strong publication record in significant conferences like CVPR or NeurIPS is preferred but not mandatory.

The work environment for the Staff Software Engineer, Perception at AeroVect is designed to be collaborative and flexible, reflecting our remote work structure. You will have the opportunity to lead autonomous projects and collaborate with a team of experts in a culture that values innovation and problem-solving. We prioritize open communication and encourage our staff to share insights and constructive feedback, contributing to a thriving, motivated team atmosphere.

Success as a Staff Software Engineer, Perception at AeroVect hinges on having a solid technical foundation in object detection models, proficiency in Python, and a keen analytical mindset. Independence in leading projects and teams is vital, along with the ability to navigate challenges creatively and effectively. Familiarity with multi-modal data and a passion for continual learning in advanced technologies will also set you apart in this innovative role.

At AeroVect, we prioritize the growth and development of our Staff Software Engineers, Perception through various initiatives. We provide access to cutting-edge technologies, encourage continuous learning through training and workshops, and support participation in major conferences. Our culture emphasizes career advancement and encourages staff to take on leadership roles and innovative projects that enhance their skill sets and contribute to the overall mission of transforming aviation.

When answering this question, focus on specific projects where you implemented 3D object detection models, discussing the techniques and tools you used, the challenges you faced, and how you overcame them. Highlight your contributions and the impact on the project outcomes.

In your response, emphasize the steps of first outlining your objectives, determining the necessary data sources, implementing data capturing tools, and establishing metrics for evaluation. Be specific about the tools and technologies you've used to create an efficient pipeline.

Here, provide examples of technical challenges such as data imbalance, model overfitting, or real-time deployment issues. Discuss the solutions you implemented and what you learned from each experience.

Discuss the methods you utilize, such as data augmentation, validation processes, and peer reviews. Emphasize the importance of quality over quantity, and provide examples of how diligent practices led to successful model outcomes.

Mention specific strategies such as attending conferences, participating in research groups, or following leading publications in the field. Describe how you integrate these learnings into your work to ensure you're using the most effective approaches and tools.

Provide a detailed account of a project that showcases your leadership skills. Discuss the project goals, the team dynamics, any obstacles encountered, and how you facilitated collaboration and problem-solving to achieve the desired outcomes.

In your response, highlight key metrics such as precision, recall, F1-score, and latency. Explain why each metric is significant and how they can affect the model's real-world application and decision-making.

Convey your systematic approach which could include data preprocessing, selecting appropriate architectures, hyperparameter tuning, and employing regular evaluation to monitor performance throughout the training process. Highlight your adaptability based on results.

Identify the tools and frameworks you have experience with, such as TensorFlow, PyTorch, or OpenCV, and provide context on how you've effectively utilized them in your previous roles, demonstrating your capabilities in tech integration.

This is your chance to reflect your enthusiasm for AeroVect, mentioning specific aspects such as its mission in autonomy and ground handling innovation. Connect your skills and experiences with how you can contribute to their vision.