Sr. Computer Vision / Machine Learning Engineer

About the company

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.

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 you'll do

Develop solutions for advanced computer vision tasks, including:

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 models

Collaborate across teams to deploy CV/ML models into production

Improve ML pipelines and infrastructure for dataset management, training, and deployment

Participate in R&D initiatives (20% of time) to experiment with state-of-the-art techniques

Drive business value by leveraging your work across robotics, software, and deployment teams

This role is in-person hybrid in Mountain View, CA. US work authorization is preferred but not required, can sponsor visas.

Must have

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

Nice to have

Experience with C/C++

Familiarity with ROS (Robot Operating System)

Knowledge of model deployment frameworks (e.g., TensorRT, RKNN, OpenVINO, ONNX, CoreML)

Experience with TypeScript/JavaScript and Django

Experience with data labelers, tooling, and data management