Computer Vision Engineer

Key Responsibilities
 
Computer Vision & Machine Learning
 

. Build and iterate on deep learning-based computer vision models for real-world imagery (OCR/STR, detection, segmentation, and tracking).
. Own dataset preparation and data pipelines (collection, cleaning, augmentation, and evaluation).
. Select and apply appropriate architectures (e.g., CNNs, Vision Transformers) and establish metrics-driven experimentation.
. Apply advanced 3D vision techniques where relevant, including 3D reconstruction (COLMAP), NeRFs, and Gaussian Splatting.

 
Software Engineering & Backend
 

. Architect and deploy scalable, low-latency ML microservices by serving computer vision models via robust REST or gRPC APIs (e.g., FastAPI, Triton Inference Server, or Node.js).
. Design and optimize high-throughput, hardware-accelerated video streaming and frame-extraction pipelines using OpenCV, FFmpeg, and GStreamer to ensure real-time processing.

 
Deployment & MLOps
 

. Optimize trained models for inference speed, throughput, and memory footprint using runtimes such as TensorRT, ONNX Runtime, CoreML, or MLX.
. Containerize and orchestrate model serving and supporting services using Docker and Kubernetes (or cloud-native serverless), with efficient GPU scheduling and edge hardware utilization.
. Build CI/CD pipelines for model retraining, testing, versioning, and production rollout, including monitoring and rollback strategies.
. Deploy and optimize computer vision systems for edge devices and resource-constrained environments.

 
Required Qualifications
 

. Education & experience: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field; 3+ years of experience shipping ML/CV products.
. Programming: Strong proficiency in Python; experience with at least one systems or backend language (e.g., C++, TypeScript, Swift, or Rust).
. ML/CV stack: Deep expertise in PyTorch or TensorFlow; experience with OpenCV.
. Backend & cloud: Experience building web services, managing API integrations, and designing cloud architectures on AWS, GCP, or Azure.
. MLOps: Experience with Git, Docker, and experiment/model tracking tools (e.g., MLflow, Weights & Biases).

 
Preferred Qualifications
 

. Experience deploying computer vision models to NVIDIA Jetson, Apple Silicon and mobile platforms (iOS/CoreML).
. Experience integrating Vision-Language Models (VLMs) and building agentic AI systems that leverage visual inputs.