Computer Vision Engineer

THE ROLE:

We’re looking for Computer Vision Engineers who enjoy turning challenging perception problems into reliable, production-ready solutions. You’ll work across the Computer Vision and Deep Learning stack - building and fine-tuning models, evaluating performance, optimizing inference, & integrating solutions into real-world video analytics applications.

The challenge isn’t simply getting a model to work. It’s making it work reliably in the real world — across different environments, hardware configurations, and deployment conditions, while balancing accuracy, latency, memory, throughput, and reliability.

You’ll work closely with our Computer Vision, Platform, Product, and C++ engineering teams to take solutions from implementation through optimization and into production.

WHAT YOU'LL WORK ON:

• Design, build, and optimize vision algorithms and deep learning models for real-time video analytics.
• Develop solutions across object detection, tracking, re-identification, pose estimation, recognition, segmentation, and anomaly detection.
• Select, adapt, and fine-tune deep learning architectures based on the requirements of a specific problem and deployment environment.
• Work with real-world datasets to train, evaluate, benchmark, and improve model performance.
• Analyze model and inference bottlenecks across accuracy, latency, memory, throughput, and compute utilization.
• Optimize models for deployment using techniques such as quantization, pruning, model simplification, and architecture optimization.
• Collaborate with C++ and systems engineers to integrate models into production pipelines and deliver validated model artifacts such as ONNX models.
• Debug challenging detection and perception failures, identify root causes, and build robust solutions.
• Develop evaluation workflows and performance benchmarks to measure and continuously improve our Computer Vision systems.
• Evaluate new Computer Vision techniques and technologies based on their practical applicability and potential product impact.
• Take ownership of problems from implementation → validation → optimization → integration → deployment.

WHAT WE'RE LOOKING FOR

You don’t need to know everything. But you should have strong technical fundamentals, practical implementation skills, and enjoy figuring things out when the answer isn’t obvious.

Core Skills

• Strong fundamentals in Machine Learning, Deep Learning, and Computer Vision.
• Good understanding of the mathematics behind ML - particularly linear algebra, probability, and calculus.
• Hands-on experience with PyTorch or TensorFlow.
• Solid understanding of Computer Vision concepts including image processing, feature extraction, object detection, tracking, segmentation, and recognition.
• Strong programming skills in Python.
• Practical experience with OpenCV and NumPy.
• Familiarity with scikit-learn is a plus.

It’s a Plus If You Have

• Experience with C++ and/or GPU programming.
• Experience deploying models using TensorRT, ONNX Runtime, or similar inference frameworks.
• Exposure to model optimization and edge AI.
• Experience working with data pipelines, annotation workflows, evaluation frameworks, or large-scale datasets.
• Familiarity with modern architectures such as Transformers, VLMs, or multimodal models.
• Experience taking models from prototype to production.
• Experience diagnosing performance issues across the model, inference stack, and underlying hardware.
• Contributions to open-source ML/CV projects, technical competitions, or strong personal projects demonstrating practical implementation skills.