Senior Robot Perception Engineer

As a Senior Robot Perception Engineer at Simbe, your responsibilities include owning and optimizing the entire object detection model pipeline from data collection and annotation to model training and validation. You'll also be tasked with integrating perception models into robotic systems, conducting R&D on advanced capabilities like using the NVIDIA Isaac toolkit, and ensuring the efficient and accurate performance of deep learning architectures within our robotics framework.

To qualify for the Senior Robot Perception Engineer position at Simbe, candidates should possess over 5 years of experience in robotic perception, computer vision, or deep learning, along with strong proficiency in Python and C++. Familiarity with ROS and hands-on experience with deep learning frameworks like TensorFlow and PyTorch are essential. Additional skills in deploying real-time AI models on embedded systems, as well as debugging competencies in a Linux-based environment, are highly preferred.

Candidates for the Senior Robot Perception Engineer role at Simbe should be familiar with technologies such as object detection, semantic segmentation, and depth estimation techniques. Experience with NVIDIA Isaac SDK, stereo cameras, LiDAR, and high-performance computing are highly beneficial, particularly in relation to real-time AI deployment in robotics.

The salary range for the Senior Robot Perception Engineer position at Simbe is between $130,000 and $200,000 per year. This base salary is competitive and can vary based on location, skills, and experience. The overall compensation package can also include equity compensation and a full benefits suite.

At Simbe, the work culture for a Senior Robot Perception Engineer is dynamic and inclusive. You will find a team-driven environment that values transparency, innovation, and accountability. Employees are encouraged to embrace challenges, collaborate effectively, and stay receptive to feedback, ensuring not just personal growth but also team success.

When discussing model training and validation, it's essential to highlight your systematic approach. Mention how you gather data, annotate it for clarity, and ensure the quality and diversity of training datasets. Emphasize the methodologies you use for validation, such as cross-validation or performance metrics, and how you iterate models based on test results to enhance their accuracy and reliability in a robotic context.

In responding to this question, discuss your experience working collaboratively with engineers from different fields, such as software and hardware. Explain how you conduct integration tests, handle compatibility checks, and resolve any discrepancies that arise during the integration process. Highlight your proactive troubleshooting methods and your focus on seamless functionality in robotic systems.

Be authentic in sharing your experiences with frameworks like TensorFlow and PyTorch. Detail the criteria you use to select architectures—such as considering the type of data you're working with, real-time requirements, and resource constraints—and mention projects where your choice led to successful outcomes.

When asked this, explain your proficiency with the Robot Operating System (ROS) and how it acts as a backbone for robotic development. Showcase your hands-on experience in using ROS to create nodes for perception tasks and how you leverage existing libraries to expedite development while ensuring performance.

Share specific challenges you encountered, such as obstacles in data collection or model accuracy issues. Discuss the strategies you implemented to resolve these challenges, such as data augmentation techniques, improved error analysis, or collaborative efforts to solicit diverse perspectives, showcasing your problem-solving abilities.

Discuss your proactive approach to staying informed about advancements in robotics, such as attending conferences, publishing papers, participating in online courses, and engaging with communities. Emphasize how you apply new knowledge and techniques to your work at Simbe, contributing to a culture of innovation.

Illustrate the importance of collaboration in your role. Mention specific examples where teamwork led to successful project completion, showcasing how exchanging ideas, open communication, and fostering a collaborative environment contribute to enhanced robotic capabilities.

When addressing this question, articulate your hands-on experience with deploying AI models on embedded systems. Discuss the tools and frameworks you utilized, any challenges regarding resource constraints or latency requirements, and how you optimized models to ensure efficient performance on various hardware platforms.

Prepare to discuss a specific project where you ventured into advanced perception techniques, such as visual SLAM or depth estimation. Detail your research approach, the experiments you conducted, and the implications your findings had on the project's success and overall robotic functionality.

Express your passion for robotics as an evolving field. Discuss how the prospect of contributing to innovative solutions that improve human interactions with technology inspires you, along with a desire to solve real-world problems through advanced robotic perception technologies.