Oversee the entire perception system development life cycle, from problem definition to deployment and ongoing improvement.

Lead a team of computer vision and perception engineers to develop and refine the system in a hands-on manner.

Spearhead the development of robust computer vision algorithms for object detection, tracking, semantic segmentation, and classification.

Champion the development and training of deep learning models for complex urban scene perception and real-time analysis.

Collaborate with cross-functional teams (cloud/device) for seamless integration and monitoring of perception models.

Analyze data to identify performance bottlenecks and opportunities for enhancing the perception system.

Foster automation in the improvement cycles of deep learning models used within the perception system.

Communicate technical findings and insights effectively to stakeholders across the company to drive performance improvements.

Utilize data visualization tools to present complex information clearly for informed decision-making.

Ph.D. or Master's in Robotics, Machine Learning, Computer Science, Electrical Engineering, or a related field.

2+ years leading and managing teams focused on developing real-world computer vision and perception systems using deep learning on edge devices.

Proven ability to deploy these systems with:

Deep Learning Frameworks: Expertise in PyTorch or TensorFlow (one mandatory, familiarity with both a plus).

Computer Vision Libraries: OpenCV.

Deployment Optimization Tools: TensorRT.

Strong Python programming and software design with experience in Pandas.

Experience deploying DL models to run on real-world, resource-constrained, systems with a pragmatic approach towards problem-solving.

Demonstrated proficiency in data science and traditional machine learning (SVMs, Random Forests). Prior experience with automated machine learning pipelines is desirable.

Proven industry track record with experience in:

Automated data annotation for computer vision.

Training multi-task and semi-supervised deep learning models for video data.

Familiarity with designing multi-modal deep learning models incorporating temporal context and geometrical constraints is a plus.