Computer Vision Engineer

Responsibilities:

• Own computer vision problems end-to-end from problem framing and data strategy through model development, edge deployment, and production monitoring.
• Build models for hard vision challenges: novel defect types, extreme class imbalance, multi-camera fusion, low-light / high-noise factory environments, and real-time inference on constrained edge hardware.
• Stay at the cutting edge of CV research and rapidly evaluate and adopt new models and techniques: YOLO26 SAM 3 Vision Transformers (DINOv2 Swin), Grounding DINO, RFDETR, zero-shot / open-vocabulary detection (YOLO-World, CLIP), translating papers into production value.
• Follow and contribute to engineering standards for the vision stack model training pipelines, data versioning (DVC), annotation workflows (CVAT, Roboflow, Label Studio), experiment tracking (W& B, MLflow), edge export formats (TensorRT, ONNX, OpenVINO), and CI/CD for model updates.
• Drive inference optimisation, quantisation (INT8 / FP16 GPTQ), pruning, knowledge distillation, and batching strategies to meet latency and cost targets across NVIDIA Jetson, industrial PCs, and cloud GPU instances.
• Debug production issues on live customer deployments, trace performance drops, root-cause failure modes, and ship fixes with the right guardrails.
• Champion a data-centric AI approach: invest in annotation quality, active learning, synthetic data generation, and feedback loops from production rather than only chasing bigger models.
• Build robust evaluation frameworks: domain-specific metrics, A/B testing against production baselines, and systematic failure-mode analysis to ensure models deliver real business impact.
• Partner cross-functionally with product, field engineering, operations, and leadership to translate business problems into well-scoped modelling projects and communicate results clearly.

Requirements:

• Bachelor's in Computer Science, AI/ML, Electrical Engineering, or a related field.
• 1-3 years of hands-on experience in computer vision with a strong track record of taking models from research/prototyping through to production deployment.
• Deep proficiency in Python and PyTorch; strong working knowledge of OpenCV, Albumentations, and image/video processing fundamentals.
• Demonstrated expertise across multiple CV tasks: object detection, instance / semantic/panoptic segmentation, anomaly detection, pose estimation, or tracking.
• Hands-on experience with modern model families YOLO (v8 / v11 / v26), transformer-based detectors (RT-DETR, DETR, RF-DETR), segmentation models (SAM / SAM 2), and CNN backbones (ResNet, EfficientNet, ConvNeXt, Vision Transformers).
• Production experience deploying models to edge or on-prem hardware using TensorRT, ONNX Runtime, or OpenVINO; comfort with Docker, Kubernetes, and at least one cloud platform (AWS / Azure / GCP).
• Strong first-principles problem-solving, comfortable navigating novel, unstructured problems where no playbook exists.
• Experience in a high-growth start-up or similarly fast-paced environment.
• Excellent communication: able to distil complex technical concepts for non-technical stakeholders, write clear documentation, and present results to leadership and customers.