Hire Computer Vision Engineersin 2 weeks
BearPlex computer vision engineers build production CV systems: object detection, image classification, OCR, video understanding, multimodal LLMs, edge deployment. Both classical CV pipelines and modern vision-language models.
What a Computer Vision Engineer actually does at BearPlex
A computer vision engineer at BearPlex covers the full CV stack: classical computer vision pipelines (OpenCV preprocessing, classical detection algorithms), deep learning CV models (CNNs, Vision Transformers, segmentation, object detection), modern vision-language models (CLIP, GPT-4V, Claude vision, Gemini), and the production engineering required to deploy CV at scale. They've shipped: real-time object detection on edge devices, document understanding pipelines for insurance and legal, defect detection systems for manufacturing QA, video analytics for retail and security, multimodal RAG systems combining images and text. They know when to use a fine-tuned YOLO model (low latency, high volume, well-defined object types) vs when to use GPT-4V or Claude vision (zero-shot understanding, novel object categories, complex visual reasoning). They handle the operational challenges that distinguish production CV from research demos: model serving on GPUs and edge devices, dataset annotation strategies, evaluation across diverse imaging conditions, and the domain shift problems that kill most CV deployments.
Sample engineer profiles
Anonymized to respect engineer privacy. Full bios shared under NDA during scoping.
Built defect detection for a manufacturing client: 99.2% recall on critical defects, runs on edge devices at 30 FPS, deployed across 12 production lines.
Shipped document understanding for a legal-tech client: extracts tables, signatures, and structured fields from 14 contract types with field-level human-in-the-loop review.
Designed multimodal RAG over 200K product catalog images and descriptions: visual-search-first ecommerce experience with 32% conversion lift on engaged sessions.
Led CV pipeline for a US healthcare imaging startup: model passed FDA SaMD review, currently deployed across 8 hospital networks.
Skills matrix
The capabilities every BearPlex Computer Vision Engineer brings on day one.
| Skill | Proficiency | Typical tools |
|---|---|---|
| Object detection (YOLO, DETR, RetinaNet) | Expert | YOLOv8/v9 · RT-DETR · Detectron2 · MMDetection |
| Image classification and embedding | Expert | timm · Vision Transformers · EfficientNet · CLIP |
| Image segmentation (semantic, instance, panoptic) | Expert | SAM 2 · Mask2Former · DeepLab · U-Net |
| OCR and document understanding | Expert | PaddleOCR · Tesseract · AWS Textract · Azure Document Intelligence · LayoutLM |
| Vision-language models (multimodal LLMs) | Advanced | GPT-4V · Claude vision · Gemini · LLaVA · Qwen-VL |
| Video understanding and tracking | Advanced | DeepSORT · ByteTrack · VideoMAE · X-CLIP |
| Production CV serving (GPU and edge) | Expert | Triton Inference Server · ONNX Runtime · TensorRT · OpenVINO |
| Edge deployment (Jetson, Coral, mobile) | Advanced | NVIDIA Jetson · Coral TPU · Core ML · TensorFlow Lite |
| Dataset annotation and curation | Expert | CVAT · Label Studio · V7 · Roboflow |
| Domain adaptation and dataset shift | Advanced | test-time adaptation · active learning · synthetic data generation |
| Augmentation and synthetic data | Expert | Albumentations · imgaug · Stable Diffusion for synthetic data |
| Quantization and model optimization | Advanced | PyTorch quantization · TensorRT INT8 · ONNX optimization |
How we vet computer vision engineers
Technical screen
60-minute deep-dive on past CV work. We probe model selection, dataset construction, evaluation methodology, and production behavior. We screen out engineers whose CV experience is academic only: production CV is dominated by data and operations problems, not model architecture.
Live CV exercise
We give the candidate a CV problem (object detection or classification on real-world messy data) with 90 minutes. They must choose architecture, train a baseline, evaluate, and discuss what they'd do to improve. We're looking for: pragmatic model selection, rigorous evaluation, and awareness of common production failure modes.
Architecture interview
Whiteboard a CV system for a realistic scenario: manufacturing defect detection, 30 FPS on edge devices, 99%+ recall on critical defects, 12 production lines, ongoing model updates. We probe for: edge vs cloud trade-offs, evaluation methodology, dataset strategy, and operations.
Reference checks + paid trial
Two engineering reference checks plus a 21-day paid trial on a real client engagement. We don't take engineers off trial until both Hamad and the client engineer report 'I want this person on the team next sprint.'
What clients say
“We'd been told fine-tuning YOLO for our defect detection was straightforward. The BearPlex engineer fixed the actual problem (our annotation guidelines were inconsistent) and got us from 78% to 99% recall by improving the dataset, not the model.”
“Their CV engineer shipped a model that passed FDA SaMD review on the first submission. The documentation and validation rigor he brought was the difference between getting cleared and not.”
“Best edge CV deployment work I've seen. The model runs at 30 FPS on Jetson Nano while maintaining accuracy: that's not magic, that's engineering discipline.”
Hiring computer vision engineers: questions answered
Yes: common in manufacturing, retail, and IoT engagements. We've deployed to NVIDIA Jetson (Nano, Xavier, Orin), Google Coral TPU, Apple Core ML on iOS, Android with TFLite, and embedded ARM processors. We handle the model optimization (quantization, pruning, ONNX/TensorRT conversion) plus the engineering work of running CV reliably in resource-constrained environments.
Depends on the workload. Fine-tuned YOLO (or similar): high volume, low latency, well-defined object categories, edge deployment. GPT-4V / Claude vision: lower volume, higher per-inference value, novel/changing object categories, complex visual reasoning, zero-shot tasks. Hybrid pipelines are common: vision-language model for hard cases, fine-tuned detector for the high-volume easy cases.
Yes: for healthcare imaging clients, we work within FDA Software-as-Medical-Device (SaMD) frameworks and have shipped models that have passed FDA review. We handle the validation documentation, dataset curation rigor, performance characterization across patient populations, and ongoing monitoring required for clinical deployment.
Yes: increasingly common. CLIP-based image embeddings combined with text embeddings in a hybrid index; query can be text, image, or both. Useful for ecommerce visual search, content moderation, and knowledge bases that include diagrams or screenshots.
Several approaches depending on volume and budget. For small datasets, our team annotates with appropriate quality control. For larger datasets, we work with annotation services (Scale AI, Surge, Labelbox) and manage the process, including writing annotation guidelines, training annotators, and running QC. For specialized domains (medical, legal), we structure annotation to involve subject matter experts at the right depth.
Primarily Lahore, Pakistan (HQ) with client-facing presence in Austin and Doha. Time zone overlap with US clients is 5-9 hours; we structure engagements with daily 2-3 hour overlap windows for synchronous work, async handoff for the rest.
Yes, when the problem benefits. For domains where real data is scarce or labeled data is expensive (medical imaging, manufacturing defects, edge cases for autonomous systems), we generate synthetic data via procedural generation, GANs, or diffusion models. We're also pragmatic: synthetic data helps in some cases and hurts in others (domain gap), and we evaluate on real data.
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