Perception and Computer Vision Engineer

As a Perception and Computer Vision Engineer at Pickle, you'll be responsible for developing and improving computer vision solutions that enhance the performance of our robotic systems. Key duties include building perception solutions for 3D object detection, testing and iterating on these solutions to boost reliability, and collaborating with other teams for seamless integration.

To qualify for the Perception and Computer Vision Engineer role at Pickle, candidates need at least 1 year of experience in computer vision or robotics software, along with a bachelor’s degree in Computer Science, Applied Mathematics, Robotics or a related field. Strong knowledge of software engineering principles, numerical linear algebra, and proficiency in Python are essential.

A Perception and Computer Vision Engineer at Pickle plays a crucial role in shaping the future of warehouse automation by developing systems that improve our robots’ object detection capabilities. Your work will directly impact the efficiency and safety of truck unloading processes, helping reduce reliance on manual labor while maximizing productivity.

Preferred programming skills for a Perception and Computer Vision Engineer at Pickle include strong proficiency in Python, and familiarity with at least one compiled language such as C, C++, or Rust. These skills are vital for developing efficient and maintainable robotics software.

At Pickle, a Perception and Computer Vision Engineer will work on various exciting projects related to robotic automation. These projects may involve developing solutions to complex 3D object detection problems, enhancing perception systems for better accuracy, and collaborating across teams to integrate perception solutions into larger system architectures.

Discuss any projects where you've utilized computer vision techniques, such as object detection, recognition, or tracking. Highlight specific technologies and tools you've worked with, and explain how your contributions improved project outcomes.

Choose a specific problem related to computer vision or robotics. Explain the situation, the steps you took to analyze and tackle the challenge, and the results of your intervention, demonstrating your problem-solving skills.

Outline your process for validating algorithms, from initial unit testing to integration testing. Mention the metrics you track for performance, and share examples of how you've iterated on solutions based on feedback and test results.

Discuss your familiarity with frameworks like OpenCV, Tensorflow, or PyTorch for 3D object detection. Provide insights into specific projects where you've applied these tools and the impact of your work.

Share how you keep abreast of industry trends, such as following relevant journals, attending conferences, participating in online forums, or contributing to open-source projects. Mention any recent advancements you found particularly interesting.

Talk about your familiarity with machine learning concepts applied to computer vision. Include specific projects where you utilized ML algorithms, emphasizing any innovative solutions you've contributed to.

Mention your key programming languages, highlighting your proficiency in Python and any compiled languages like C or C++. Discuss how you've leveraged these languages in previous projects to solve computer vision tasks.

Emphasize your communication skills and collaborative mindset. Provide examples of past experiences where you worked alongside software and hardware teams to achieve project goals, demonstrating how teamwork has enhanced your projects.

Explain the significance of numerical linear algebra in computer vision algorithms, such as those used for transformations, image processing, and 3D modeling. Highlight how your knowledge in this area has been applied in your work.

Discuss your time management strategies and how you prioritize tasks based on project deadlines, complexity, and team collaboration needs. Include an example to show how you effectively manage multiple responsibilities.