Sr. Computer Vision / Machine Learning Engineer
Corvus Robotics12 months ago
Responsibilities
- Develop computer vision solutions for monocular and stereo depth estimation, learning-based structure-from-motion, 3D occupancy networks, scene understanding, and object detection.
- Optimize compute-constrained models for performance, accuracy, and speed.
- Collaborate across robotics, software, and deployment teams to deploy computer vision and machine learning models into production.
- Improve ML pipelines and infrastructure for dataset management, training, and deployment.
- Participate in R&D initiatives for 20% of the role to experiment with state-of-the-art techniques.
- Drive business value through computer vision and machine learning work across robotics, software, and deployment functions.
Requirements
- 5+ years of industry experience in computer vision or machine learning.
- Strong Python and PyTorch experience with expertise in 2D and 3D computer vision techniques.
- Proficiency with Linux, Git, AWS or GCP, and CI/CD workflows.
- Experience optimizing deep neural network performance for both training and inference.
- Knowledge of knowledge distillation, model quantization, and network pruning for embedded systems.
- Experience with C or C++ is preferred.
- Familiarity with ROS is preferred.
- Knowledge of TensorRT, RKNN, OpenVINO, ONNX, or Core ML is preferred.
- Experience with TypeScript, JavaScript, Django, data labelers, tooling, and data management is preferred.
- Ability and desire to assume responsibility in a fast-paced startup environment.
Benefits
- In-person hybrid work arrangement in Mountain View, California.
- US work authorization is preferred but not required, and visa sponsorship is available.
Tech Stack
Categories
ML EngineeringRobotics
About Corvus Robotics
Corvus Robotics builds autonomous drones and software that scan, count, and track palletized inventory for warehouses and distribution centers. Its systems perform cycle counting and deliver real-time inventory data to supply chain, retail, and 3PL operators, improving safety and labor efficiency. Founded in 2017 and headquartered in San Francisco, the privately held company reports deployments at large U.S. enterprises and is scaling installations across hundreds of facilities.