Explain your VLM project end-to-end

You will be asked to give a deep-dive (a “resume grilling”) on a Vision-Language Model (VLM) project listed on your resume. Address the following clearly and with specifics:

1. Problem and scope
   • Which task or tasks did the VLM handle (for example, captioning, VQA, retrieval, grounding, OCR plus reasoning)?
   • How was success defined (offline metrics, product metrics, or both)?
2. Model architecture
   • Overall structure (vision encoder, language model, fusion mechanism).
   • Where fusion occurs (early or late; cross-attention; adapters; projection layers).
   • Which components were frozen and which were trainable.
3. Data and distribution
   • Which datasets you used (public, internal, or both).
   • Label formats (pairs, dialogs, preferences, bounding boxes, masks).
   • Data distribution and known biases (domains, languages, image types, long-tail).
   • How you split train/val/test and prevented leakage.
4. Training recipe
   • Objective or objectives: contrastive, next-token prediction, instruction tuning, RLHF/DPO, multi-task.
   • Pretraining versus finetuning stages.
   • Important hyperparameters and infrastructure (batching, mixed precision, sequence length, curriculum).
   • Evaluation: which benchmarks, ablations, and error analyses were used.
5. End-to-end versus modular
   • Was the system trained end-to-end? If not, which parts stayed fixed and why?
   • Trade-offs: stability, compute, data requirements, and adaptability.
6. Inference time / latency
   • Where inference time goes (vision encoder, KV-cache, decoding).
   • Throughput and latency numbers, and how you measured them.
   • Optimizations attempted (quantization, speculative decoding, caching, batching).
7. Limitations and improvements
   • Known failure modes (hallucination, OCR errors, spatial reasoning, counting, bias, adversarial images).
   • Specific proposals for improvement (data, architecture, training, evaluation, serving).

Respond as you would in an onsite interview: be concise, technical, and include specific examples and numbers whenever possible.

Overview: This question assesses skill in vision-language model engineering, covering model architecture (vision encoder, language model, fusion), data curation and distribution, training recipes, evaluation metrics, inference latency, and limitations analysis in the Machine Learning area of multimodal/Vision-Language models.