What you'll do:

• Model Development & Evaluation
  • Build and maintain evaluation frameworks for document models, LLMs, OCR, and structured extraction.
  • Define metrics, benchmarks, and validation strategies for real-world document workloads.
• Dataset & Pipeline Creation
  • Design and curate high-quality datasets for supervised training, fine-tuning, and validation.
  • Create scalable preprocessing pipelines for PDFs, scans, images, forms, and semi-structured documents.
• Model Training & Fine-Tuning
  • Train and fine-tune transformer-based OCR, VLMs, layout models, and open-source LLMs for document understanding tasks.
  • Optimize models for reliability, accuracy, and cost efficiency in production environments.
• Inference & Deployment
  • Deploy ML models with modern inference runtimes (vLLM, TGI, TensorRT, ONNX Runtime).
  • Build guardrails, monitoring, and fallback mechanisms to ensure safe and predictable model behavior.
• Inference & Deployment
  • Develop retrieval and chunking strategies tailored to document structures (tables, forms, multi-page PDFs).
  • Optimize end-to-end RAG pipelines for semantic search, Q&A, and workflow automation.
• Cross-Functional Collaboration
  • Partner with PMs, backend engineers, and product designers to define AI opportunities and translate requirements into technical solutions.

Who you are:

We are expanding our AI/ML function with an ML Engineer who specializes in document intelligence, vision–language models, and LLM-based extraction and reasoning. You should be comfortable with both traditional document AI approaches and cutting-edge GenAI workflows. You thrive in fast-moving environments, are self-directed, and enjoy solving practical ML problems that directly impact customers.