Senior Software Engineer – Computer Vision

Jobverse.io; San Mateo (CA); On-site; USD 150,000 - 190,000; Full time

Job summary

Verkada's Computer Vision team seeks a Senior Software Engineer to advance AI/ML for intelligent video analysis. You will implement and optimize CV models, including people/vehicle detection and LPR/face recognition, using C++ and PyTorch/TensorFlow.

You will deploy edge-friendly solutions and contribute to traditional CV algorithms and training of deep networks. Ideal candidates have strong software craftsmanship, experience with large-scale video data, and collaboration across teams to deliver

Qualifications

• Experience building computer vision / ML models for video analytics.
• Proficient in C++ and ML frameworks (e.g., TensorFlow, PyTorch).
• Experience deploying models to edge devices or constrained environments.

Responsibilities

• Creating and refining algorithms for detection (people, vehicles) and other intelligent video analysis tools.
• Implementing and deploying a binary classifier using TensorFlow for large-scale camera data.
• Detecting unusual object addition/removal in scenes and counting frequencies.
• Training text image embeddings and vision-language models.
• Training license plate recognition models and implementing LPR on the edge.
• Training facial recognition models and implementing real-time facial recognition.
• C++ – writing clean, modular code and traditional CV algorithms.
• Training deep learning networks using PyTorch, TensorFlow, Keras, or similar.

Skills

C++

TensorFlow

PyTorch

Deep learning

Computer Vision

Edge deployment

Tools

Keras

CUDA

Job description

Verkada develops AI-powered security products and video analytics. The Senior Software Engineer on its Computer Vision team develops artificial intelligence and machine learning models and algorithms for intelligent video analysis, including detection, recognition, and counting capabilities.

Responsibilities

• Creating and refining algorithms for features such as people and vehicle detection, license plate recognition, and other intelligent video analysis tools
• Implementing and deploying a binary classifier using TensorFlow for detecting the binary states across millions of cameras
• Detecting unusual object addition/removal in a scene
• Detecting and counting object and people frequencies
• Training text image embedding models and vision language models
• Training license plate recognition models and implementing LPR on the edge
• Training facial recognition models and implementing real-time facial recognition
• C++ – writing clean, modular, C++ code
• Traditional computer vision algorithms
• Training deep learning networks using PyTorch, TensorFlow, Keras, or similar
• Data structures and architecture