Job Title

Role SummaryYou will own the full ML stack that turns raw dielines, PDFs, and e-commerce images into a self-learning system that reads, reasons about, and designs packaging artwork.That includes:building data-ingestion & annotation pipelines (SVG/PDF → JSON),designing / modifying model heads on top of LayoutLM-v3, CLIP, GNNs, diffusion LoRAs,training & fine-tuning on GPUs,shipping inference APIs and evaluation dashboards.You’ll work day-to-day with packaging designers and a product-manager; you are the technical authority on everything deep-learning for this domain.Key ResponsibilitiesData & Pre-processing (≈ 40 %) • Write robust Python scripts to parse PDF, AI, SVG; extract text, colour separations, images, panel polygons. • Implement Ghostscript, Tesseract, YOLO, CLIP pipelines. • Automate synthetic-copy generation for ECMA dielines. • Maintain vocabulary YAMLs & JSON schemas.Model R-&-D (≈ 40 %) • Modify LayoutLM-v3 heads (panel-ID, bbox-reg, colour, contrastive). • Build panel-encoder pre-train (mask-panel prediction). • Add Graph-Transformer & CLIP-retrieval heads; optional diffusion generator. • Run experiments, hyper-param sweeps, ablations; track KPIs (IoU, panel-F1, colour recall).MLOps & Deployment (≈ 20 %) • Package training & inference into Docker/SageMaker or GCP Vertex jobs. • Maintain CI/CD, experiment tracking (Weights&Biases, MLflow). • Serve REST/GraphQL endpoints that designers and the web front-end call. • Implement active-learning loop that ingests designer corrections nightly. Must-Have Qualifications5 + years Python, 3 + years deep-learning (PyTorch, Hugging Face).Hands-on with Transformer-based vision-language models (e.g. LayoutLM, Pix2Struct) and at least one object-detection pipeline (YOLOv5/8, DETR).Comfortable hacking PDF/SVG tool-chains: PyMuPDF/pdfplumber, Ghostscript, svgpathtools, OpenCV.Experience designing custom heads / loss functions and fine-tuning large pre-trained checkpoints on limited data.Solid Linux & GPU know-how; can spin up, monitor, and profile multi-GPU jobs.Familiarity with graph neural networks or relational transformers.Clear, idiomatic Git & code-review discipline; writes reproducible experiments.Nice-to-HaveKnowledge of colour science (Lab, ICC profiles, Pantone tables) or print production.Prior work on multimodal retrieval (CLIP, ImageBind) or diffusion fine-tuning (LoRA, ControlNet).Packaging / CPG industry exposure (Nutrition Facts, Drug Facts, ECMA codes).Experience standing up FAISS or similar vector search, and with AWS/GCP ML tooling.Familiarity with Typescript/React front-ends for quick label-preview UIs.Primary Tool Stack You’ll Own under the following domains: DL frameworks PyTorch, Hugging Face Transformers, torch-geometricParsing / CV PyMuPDF, pdfplumber, svgpathtools, OpenCV, GhostscriptOCR / Detectors Tesseract, YOLOv8, Grounding DINO (optional)Retrieval CLIP / ImageBind + FAISSMLOps Docker, GitHub Actions, W&B or MLflow, AWS SageMaker / GCP VertexLanguages 95 % Python, occasional Bash / JSON / YAML Deliverables in the First 6 MonthsData pipeline v1 that converts > 500 ECMA dielines + 200 PDFs into training-ready JSON.Panel-encoder checkpoint with MVP copy-placement model (LayoutLM-v3 backbone + heads) hitting ≥ 85 % IoU on validation.REST inference service + designer preview UI able to draft lid/side-wrap artwork for one SKU in Nightly active-learning retrain loop.Reporting & TeamReports to Head of AI (or CTO).Collaborates with 1 front-end engineer, 1 product manager, 2 packaging-design SMEs.