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, RF DETR, 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.