Hire Computer Vision Developers

Strong candidates discuss speed versus accuracy (YOLO is fast, Faster R-CNN is accurate, EfficientDet balances both), single-stage versus two-stage detectors, and when each makes sense. They connect it to real constraints, real-time video versus batch processing, edge devices versus cloud.

Experienced developers discuss class imbalance strategies, focal loss, hard negative mining, adjusting sampling during training, and evaluation metrics beyond accuracy (precision-recall curves, F1 score). Watch for understanding that training on imbalanced data requires specific techniques.

This reveals deployment knowledge. They should discuss model optimization (quantization, pruning), runtime choices (TensorFlow Lite, CoreML, ONNX), on-device inference versus cloud, and fallback strategies. Listen for practical experience with mobile constraints.

Practical candidates check for train-test distribution mismatch, look at which examples fail in production, investigate data quality issues, and consider domain shift. This shows systematic debugging versus random hyperparameter tuning.

Strong answers investigate model size versus accuracy trade-offs, implement quantization or pruning, use model distillation, batch requests intelligently, and consider cheaper models for easy examples with complex models for hard cases. Avoid candidates who say "just get bigger GPUs."

Their definition of challenging matters. Data collection? Model architecture? Deployment constraints? Strong candidates explain specific problems they solved, how they evaluated success, and what they learned. Vague answers about "achieving high accuracy" signal thin experience.

Experienced developers acknowledge most cases benefit from transfer learning. They discuss scenarios where it helps (limited labeled data, similar domains), when fine-tuning versus feature extraction matters, and rare cases where training from scratch makes sense. This reveals practical judgment.

Good answers: build quick prototypes to show capabilities, explain limitations through concrete examples, propose alternatives when requests aren't realistic, and set expectations on data requirements. They help teams understand CV possibilities without gatekeeping.

What do they focus on? Label quality? Annotation guidelines? Inter-annotator agreement? Good answers mention catching labeling errors early, iterating on guidelines, and understanding that model performance depends on data quality. Listen for attention to data quality.

Neither answer is wrong. But if you're scaling production systems and they only want research work, that's a mismatch. Watch for self-awareness about preferences and whether they align with your needs.

Strong candidates have systems, following specific researchers or topics, reading papers selectively based on relevance, implementing techniques on side projects to understand them. Avoid candidates who claim to read everything or ignore research entirely.