Sr. Perception Applied Scientist

As a Senior Perception Applied Scientist, you will be at the forefront of developing and implementing advanced vision algorithms for robotic manipulation and navigation. Your role will involve collaborating with teams to create interfaces between robot perception and skilled task execution, designing object representations, and integrating existing algorithms into our robotic hardware. Additionally, you'll mentor junior engineers and ensure quality code practices through regular reviews.

To qualify for the Senior Perception Applied Scientist role, applicants should hold a BS, MS, or PhD in computer science, robotics, electrical/mechanical/aerospace engineering, or a related field. You should also have at least 5 years of experience in a related industry, with strong programming abilities in Python and/or C++, and experience with ROS 2 and robotic software integration.

Expertise in programming is crucial for the Senior Perception Applied Scientist role. Candidates must have strong skills in Python and/or C++, as these languages are essential for developing and incorporating advanced vision algorithms. Familiarity with ROS 2 and the practical application of these technologies in a robotics setting is also required.

Mentorship experience is highly valued for the Senior Perception Applied Scientist position. As part of your responsibilities, you will be guiding and mentoring junior engineers and researchers, which not only helps in skill development within the team but also enhances the quality and reliability of our projects through shared knowledge.

While specific required experiences include a robust background in computer vision technologies, preferred experiences for the Senior Perception Applied Scientist role may include working with deep learning frameworks such as PyTorch and possessing a proven track record in deploying research-level computer vision systems on physical robotic hardware.

When answering this question, focus on specific projects where you've implemented computer vision systems. Discuss the challenges you faced, your approach to problem-solving, and the outcomes of your efforts. This shows not only your technical skills but also your ability to apply them in practical situations.

Talk about the strategies you use for code quality assurance, such as writing unit tests, conducting code reviews, and adopting best coding practices. Highlight any mentorship roles where you've helped maintain code reliability within a team setting.

Provide insights into how you've worked on object representations or scene graph estimations in past projects. Discuss specific algorithms you've utilized, any unique challenges faced, and how you overcame them. Demonstrate your depth of knowledge in these essential computer vision techniques.

Share your preferred methodologies by discussing frameworks or algorithms you've successfully employed. It's beneficial to mention any specific outcomes achieved from these estimators, as well as how they improved robotic navigation or manipulation tasks.

Discuss your mentoring philosophy and specific methods you use to support junior engineers. Mention any successful mentoring experiences, highlighting how you've fostered a positive learning environment and contributed to their professional growth.

Address your hands-on experience with ROS 2, detailing projects where you've integrated robotic software. Emphasize how you tackled integration challenges, what you've learned from those experiences, and the impact it had on your team or project's success.

Answer this by describing a specific challenging problem, the technical approach you took to solve it, and ideally, the results or improvements achieved in robot performance. This showcases both your problem-solving skills and technical expertise.

Mention your strategies for keeping current in the field, such as attending conferences, participating in online courses, engaging with professional communities, or following influential researchers. Highlight how you’ve applied new knowledge to your work.

Provide concrete examples of modular software systems you’ve worked on, explaining their architecture, purpose, and how they facilitated easier integration or maintenance in projects. Focus on teamwork and collaborative efforts if applicable.

Highlight specific advanced techniques you are experienced with, such as object pose estimation or Gaussian splatting. Discuss how you implemented them in your work, and the results these techniques helped achieve.