Autonomous inventory intelligence for the physical economy. Corvus Robotics drones and AI forklift sensors count inventory in Fortune 500 warehouses. Backed by Y Combinator.
About the role
We are hiring Computer Vision / Machine Learning Software Engineers to build compute-constrained models for deployed robots. You'll tackle diverse technical challenges, working with vast amounts of sensor data to increase environmental awareness and provide customers with deeper inventory insights. We value problem-solving, innovation, and continuous learning, and we encourage exploring new technologies to advance our machine learning capabilities.
What they're looking for
- 5+ years of industry experience in Computer Vision / Machine Learning, Python/PyTorch
- Expertise in 2D and 3D computer vision techniques
- Proficiency with Linux, Git, AWS/GCP, and CI/CD workflows
- Experience in performance engineering for deep neural networks (both training and inference)
- Knowledge of model optimization techniques for embedded systems, including knowledge distillation, model quantization, and network pruning
- Adaptive and desire to assume responsibility in a fast-paced startup environment
More about this role
Every physical good spends time in a warehouse, and every warehouse tracks their inventory. Today, nearly 100% of warehouses track their inventory manually using barcode scanners and climbing forklifts.
We're Corvus Robotics . Our fully autonomous Corvus One ™ drones use computer vision & robotics to automatically track inventory, improving worker safety and increasing labor efficiency. We believe that data-driven, safe inventory management will optimize the global physical economy and improve economic prosperity for humanity.
We are hiring Computer Vision / Machine Learning Software Engineers to build compute-constrained models for deployed robots. You'll tackle diverse technical challenges, working with vast amounts of sensor data to increase environmental awareness and provide customers with deeper inventory insights. We value problem-solving, innovation, and continuous learning, and we encourage exploring new technologies to advance our machine learning capabilities.
Monocular and stereo depth estimation
Learning-based structure-from-motion
3D occupancy networks
Scene understanding
Object detection
Optimize performance, accuracy, and speed of compute-constrained...
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