Senior Software Engineer, Autonomy - Perception, Deep Learning

Cyngn•Mountain View, CA 94035, US• Full Time

Posted 8 months ago• Software• Software

Compensation

$180,000–$198,000/year

Job Description

About Cyngn

Based in Mountain View, CA, Cyngn is a publicly-traded autonomous technology company. We deploy self-driving industrial vehicles like forklifts and tuggers to factories, warehouses, and other facilities throughout North America. To build this emergent technology, we are looking for innovative, motivated, and experienced leaders to join us and move this field forward. If you like to build, tinker, and create with a team of trusted and passionate colleagues, then Cyngn is the place for you. Key reasons to join Cyngn:

We are small and big.

With under 100 employees, Cyngn operates with the energy of a startup. On the other hand, we’re publicly traded. This means our employees not only work in close-knit teams with mentorship from company leaders—they also get access to the liquidity of our publicly-traded equity.

We build today and deploy tomorrow.

Our autonomous vehicles aren’t just test concepts—they’re deployed to real clients right now. That means your work will have a tangible, visible impact.

We aren’t robots. We just develop them.

We’re a welcoming, diverse team of sharp thinkers and kind humans. Collaboration and trust drive our creative environment. At Cyngn, everyone’s perspective matters—and that’s what powers our innovation.

About this role:

We are seeking an experienced Senior Software Engineer to join our Perception team within the Autonomy Software organization. This role centers on developing advanced deep learning models that power object detection, segmentation, and tracking using camera and lidar data for Cyngn’s autonomous forklifts. A key responsibility will be designing, training, and deploying multi-modal perception models to detect and localize pallets in diverse warehouse environments, enabling reliable pallet engagement and stacking.

Additional experience with model acceleration techniques, building model development infrastructure, and ownership of the end-to-end model lifecycle—from research and training through deployment in production—is considered a strong bonus.

Responsibilities

Design, implement, and optimize deep learning models for object detection, segmentation, and tracking using camera and lidar data.

Build and maintain data pipelines, training infrastructure, and inference frameworks to support reproducible and scalable model development.

Develop tools and metrics for evaluating model performance and ensuring robustness across diverse warehouse environments.

Work with third-party annotation vendors to generate high-quality labeled datasets for training and validation.

Qualifications

MS/PhD in computer science, computer engineering, robotics, or similar technical field of study.

4+ years of experience writing Python software in a production environment - unit testing, code review, algorithm performance trade-offs, etc.

Strong theoretical foundation in deep learning techniques for computer vision, with working knowledge of linear algebra, probability, and optimization.

Hands-on experience developing and deploying deep learning models for real-world perception tasks (e.g., detection, segmentation, multi-object tracking).

Proficiency with libraries such as Pytorch, TensorFlow, Numpy, SciPy, OpenCV (Python), etc.

Experience building and integrating tools and infrastructure to optimize model development lifecycle, including but not limited to model versioning, model evaluation, model deployment, etc.

Excellent written & verbal communication skills.

Bonus Qualifications

Proficiency in C++ for robotics or real-time applications.

Hands-on experience with model acceleration or compression (TensorRT preferred).Experience utilizing foundation models (e.g., SAM, CLIP) to expedite training and development cycles.

Strong classical computer vision skills (e.g., image processing, feature extraction, geometry-based methods) to complement deep learning approaches.

Experience with leveraging IsaacSim synthetic data to augment real-world datasets and accelerate model development.

Exposure to industrial material handling autonomous vehicles including forklifts and tuggers operating in dynamic warehouse environments.