Computer Vision Engineer

• Image generation experiments for children’s book illustration workflows, style consistency, character consistency, page by page art direction
• Dataset building and curation, reference collection, captioning, prompt sets, quality checks, bias and safety checks
• LoRA training and evaluation, style LoRAs, character LoRAs, control of pose, composition, and repeatable outputs
• Prompting systems and automated prompt generation, templates, constraints, negative prompts, reproducibility
• Multimodal knowledge workflows, linking manuscript context to visual briefs, retrieval of reference guides, image and text grounding
• Rapid testing harness, A, B comparisons, scoring rubrics, human review loops, failure analysis, iteration logs
• LLMOps and MLOps basics, experiment tracking, model versioning, evaluation reports, cost and latency tracking
• Building small prototypes that connect models to product flows, API wrappers, batch pipelines, internal tools for the team
• Documentation, clear notes, reusable notebooks, repeatable scripts, simple internal guides for future interns

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AI Intern – Stable Diffusion, Generative AI Image Generation