Job description
About Doaz
- Doaz is a hyper-growth startup on a mission to turn fragmented industrial knowledge into instant, actionable insight. We build LLM- and Vision-AI solutions for construction, heavy industry, and finance leaders who must transform terabytes of drawings, specifications, and regulations into real-time decisions.
- We’re expanding our GeoAI programs (including joint work with POSCO E&C) and launching drawing-change detection services that automatically compare plan versions, detect deltas, and explain design impacts.
Why You’ll Love Working Here
• True 0-to-1 ownership — Ship models that land in production sites within weeks.
• Global impact, lean crew — 30 teammates across KR🇰🇷/PK🇵🇰/IN🇮🇳; no bureaucracy, only builders.
• Tech freedom — YOLO or RT-DETR? Gemma-VL, Qwen-VL, or LLaVA? You choose, we fund.
Role Overview
We’re hiring a Senior Computer Vision & Multimodal LLM Engineer (GeoAI & Drawing Change Analysis).
You’ll lead end-to-end development of a version-aware drawing-diff engine (PDF/DWG raster & vector), symbol/text extraction, and change-impact narratives powered by RAG/LLM. Expect fast cycles from prototype → service: detection models, OCR/layout understanding, retrieval, and explainable outputs that engineers can trust.
Key Responsibilities
Drawing Change Analysis (CV)
- Build a robust diff pipeline for architectural/structural/MEP drawings: rasterization, layer parsing, vector geometry ops, and semantic change clustering.
- Train/finetune detectors & segmenters (e.g., YOLOv8/RT-DETR/Detectron2/SAM) for symbols (columns, openings, sleeves), title blocks, and revision clouds; achieve production-grade mAP/F1.
- Implement geometry-aware post-processing (IoU/topology checks, snapping, graph connectivity) to reduce false positives.
Document & Layout Understanding
- Engineer OCR + layout models (PaddleOCR/Tesseract + DocFormer/LayoutLMv3/Donut) to read legends, notes, schedules, and BOM tables; normalize to structured JSON.
- Build version-aware entity tracking (IDs, gridlines, BH IDs, coordinates) across revisions.
GeoAI & LLM/RAG
- Design retrieval over drawings/specs (BM25 + vector) with reranking; ground LLM answers in evidence with citations and clickable locations.
- Generate change-impact summaries (e.g., slab shear reinforcement, opening proximity to columns) with rules + LLM verification; measure factual precision.
Productization & DevOps
- Ship FastAPI/gRPC microservices, batch & streaming workers (Ray/Celery), GPU inference (Triton/TensorRT), and observability (Prometheus/Grafana).
- Own evaluation: dataset curation, data labeling guidelines, ablation/A-B tests, and regression suites.
Collaboration
- Work closely with domain SMEs (geotech/structural) to encode rules (KDS/KBC, internal standards) and prioritize what matters to the field.
Minimum Qualifications
- 5+ years of production Python (3.x) building ML-heavy backends; strong PyTorch.
- 3+ years in computer vision for detection/segmentation/OCR or document AI at scale.
- Hands-on with multimodal LLM/RAG (LangChain/LlamaIndex), vector DBs (Pinecone/Weaviate/FAISS), and rerankers.
- Proven experience parsing engineering drawings or complex PDFs (vector/raster), including geometry and layout reasoning.
- Solid MLOps: reproducible training, CI/CD, model packaging, monitoring; cloud on AWS/GCP.
- Fluent written & spoken English (Korean a plus).
Preferred Extras
- GPU orchestration (Kubernetes/Ray/Slurm), high-performance inference (ONNX/TensorRT).
- Experience with VLMs (Gemma-VL, Qwen-VL, LLaVA), CLIP, or doc-layout models.
- Open-source contributions, papers, or strong public demos in CV/doc AI/RAG.
- Full-stack chops (TypeScript/Next.js/React) for quick operator tools and review UIs.
Compensation & Benefits
- Competitive base salary (market-leading) , around 20 lakh (yearly)
- Performance-based annual bonus (up to 20%).
- cloud credits, and AI tools support.
Hiring Process (≈ 2–3 weeks)
- Quick intro call (15 min, mutual fit).
- 48-hour take-home: Drawing Diff + Evidence-Grounded Summary (provide code + short README; clarity > polish).
- Deep-dive tech interview: architecture, modeling choices, evaluation, and scaling plan.
- Culture & vision chat with Founder/CEO.
- Offer — if all green, written offer within 24 h.
How to Apply
Email doaz@doaz.ai with subject [CV/LLM Engineer – Your Name] and include:
- Résumé/CV with measurable outcomes (metrics, latency, cost, accuracy).
- Current or recent salary.
- GitHub and/or live demos of CV/doc-AI/RAG work (links preferred).
- A one-page diagram of your “Drawing Revision → Detection → Evidence → LLM Narrative” pipeline, noting models, retrieval, and evaluation metrics.
Employment type: Full-time
Ready to turn messy drawings and specs into instant, trusted intelligence?
Let’s build the future together at Doaz.
