Senior Computer Vision Engineer

Senior Computer Vision Engineer — Vision Foundation Models PerPlant | Remote or Copenhagen 🌾 THE PROBLEM Agronomic advisors know exactly what field conditions they need to detect—but building a custom detector takes months and a specialized ML team. We're collapsing that to days, without requiring advisors to touch model architecture or read a precision/recall table. 🔧 WHAT WE'VE BUILT → Foundation-model backbone trained on 4 years of geotagged field imagery (200k hectares, ~1M datapoints, 2cm/px) → Data pipeline that automatically feeds field operations back into model improvement → Infrastructure for few-shot fine-tuning and compressed deployment to NVIDIA Jetson hardware 👤 WHAT WE NEED A Senior CV Engineer to own few-shot adaptation of foundation models (Grounding DINO, DINOv3, CLIP) to novel agronomic classes, knowledge distillation to edge-deployable models (YOLO, RT-DETR), and the system-level reasoning between detection, scene-level classification, and semantic search. You'll report to our VP of ML and work directly with domain experts and field operators. ✓ YOU SHOULD HAVE → Hands-on with object detection (YOLO, DETR) and foundation models (DINO, CLIP, ViT) → Shipped distillation/pruning/quantization to production hardware (Jetson, ONNX, TensorRT) → Deep PyTorch, PyTorch Lightning, distributed training (DDP) experience → Ability to explain model behavior to non-technical stakeholders without jargon (non-negotiable) → Experience with one of: multi-object tracking, instance segmentation, embeddings, or compression 🎯 NICE TO HAVE → Foundation-model adaptation (LoRA, prompt-tuning, lightweight fine-tuning) → Edge device optimization (Jetson Orin, embedded Linux) → Autonomous systems, robotics, or agricultural tech background 🧪 HOW WE HIRE Real assessment, not whiteboard puzzles: few-shot detection on a novel class, distillation to edge hardware, system design, and code review (PyTorch Lightning). 6–8 hours async + 45 min live. You own exactly what you're being assessed on. ⏱️ WHY NOW We've de-risked the foundation model and data flywheel. The constraint is now the team—building the inference-optimization layer that lets advisors iterate quickly. If you've wanted to work on vision problems millions of farmers face daily, this is the moment. → Apply here or reach out to: sumod@perplant.ai → Learn more: www.perplant.com ---

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