Multimodal AI Engineering Interview Prep
Test & Track Your Readiness

Cross-modal alignment, contrastive learning (CLIP training), the modality gap, shared embedding space geometry, why LLMs + vision encoders work, modal fusion strategies, and the landscape of multimodal models in 2025.

LLaVA architecture (projection layer, visual tokens), InternVL, Qwen-VL, Gemini multimodal interleaving, GPT-4V's approach, PaliGemma, the connector/projector design space, resolution scaling strategies, and vision token compression.

ViT patch encoding, CLIP vs SigLIP training objectives, EVA-CLIP, DINOv2 for dense prediction, resolution vs patch size tradeoffs, positional encoding for images, and choosing the right encoder for your VLM task.

Image prompting for different tasks, multi-image reasoning, visual chain-of-thought, referring expression comprehension, grounding with bounding boxes, set-of-marks prompting, OCR+VLM pipelines, and failure modes of VLM prompting.

DDPM forward/reverse process, score matching, DDIM acceleration, classifier-free guidance (CFG), latent diffusion (compressing to latent space), VAE role in LDM, noise schedules, and why diffusion beats GANs.

UNet with cross-attention for text conditioning, Diffusion Transformers (DiT, SD3, Flux), SDXL dual-encoder architecture, the shift from convolutional to transformer denoiser, Flux flow matching, and architectural decisions at scale.

ControlNet architecture (trainable copy of encoder), conditioning types (canny, depth, pose, segmentation), IP-Adapter (image prompt adapter), T2I-Adapter, style transfer approaches, multi-guidance composition, and guidance strength tuning.

Video diffusion model architecture, temporal attention layers, 3D UNet vs DiT for video, Sora's conceptual approach (spacetime patches), CogVideoX, motion prior, frame consistency challenges, inference cost, and practical video generation APIs.

Whisper architecture (encoder-decoder, mel spectrogram), streaming ASR, diarization, TTS systems (TortoiseTTS, StyleTTS2, Kokoro, XTTS), voice cloning, real-time TTS latency optimization, audio tokenization (EnCodec, DAC), and voice agent design.

Late-interaction multimodal retrieval, ColPali architecture (PaliGemma + ColBERT-style retrieval for PDFs), vision-language embeddings for retrieval, image-text cross-modal search, multimodal reranking, and building production multimodal RAG pipelines.

Modern OCR pipelines (Surya, TrOCR, PaddleOCR), layout understanding models (LayoutLM, LayoutLMv3), DocVQA with VLMs, table extraction, structured data extraction from PDFs, chart understanding, and document processing at scale.

MMMU, MMBench, SEED-Bench, MMVP, HallusionBench for VLM eval; FID, IS, CLIP Score for generation; WER/CER for ASR; MOS/UTMOS for TTS quality; and designing custom multimodal eval suites for product-specific tasks.

NSFW image detection pipelines, watermarking generated content (C2PA, invisible watermarks), jailbreak attacks unique to VLMs (typographic attacks, adversarial images), child safety classifiers, and responsible generation deployment.

LoRA/QLoRA for VLMs (LLaVA-style fine-tuning), instruction tuning with image-text pairs, LLaVA-1.5 data mixture recipe, synthetic multimodal data generation, RLHF for VLMs (RLHF-V, LLaVA-RLHF), dataset curation tools, and pitfalls.

Video LLMs (LLaVA-Video, Qwen2-VL-Video), frame sampling strategies, temporal reasoning, long-video understanding, video captioning, action recognition with VLMs, video RAG, and inference cost for video inputs.

Agents with screenshot understanding (SoM + VLM), computer use agents (Anthropic, Claude Vision), GUI agents, vision tool design (crop/zoom/annotate), combining VLM perception with action planning, and building reliable vision agents.

Image preprocessing pipelines (resizing, normalization, tiling for high-res), batching mixed image+text inputs, serving VLMs with vLLM, diffusion model serving (ComfyUI, Diffusers API), GPU memory for vision workloads, and latency profiling.

End-to-end multimodal application architecture: document processing pipeline, image-search product, video understanding platform, voice-first AI assistant, cost and latency analysis, multimodal caching strategies, and design interview patterns.