Machine Learning Engineer (GenAI)

TL;DR

Machine Learning Engineer (GenAI): Designing, training, evaluating, and optimizing models to transform unstructured documents into high-quality structured data with an accent on document understanding, structured extraction, and AI-first product experiences. Focus on bringing cutting-edge research into real production systems at scale, tackling model robustness, and deep GenAI integration.

Location: Remote from Portugal, Ukraine, Poland, or other European countries.

Company

hirify.global empowers more than 67,000 growing organizations to thrive by taking the work out of document workflow, providing an all-in-one platform to create, manage, and sign digital documents.

What you will do

• Build and maintain evaluation frameworks for document models, LLMs, OCR, and structured extraction.
• Design high-quality datasets and scalable preprocessing pipelines for various document types.
• Train and fine-tune transformer-based OCR, VLMs, layout models, and open-source LLMs for document understanding.
• Deploy ML models with modern inference runtimes, building guardrails, monitoring, and fallback mechanisms.
• Develop and optimize RAG pipelines for semantic search, Q&A, and workflow automation, tailored to document structures.
• Partner with PMs, backend engineers, and product designers to define AI opportunities and translate requirements.

Requirements

• 5+ years of Python experience.
• Experience training, fine-tuning, and deploying traditional computer vision models for document intelligence tasks.
• Hands-on experience with document understanding frameworks (LayoutLM, Donut, DocFormer) and modern vision-language models.
• Experience deploying and optimizing models using inference frameworks such as vLLM, TGI, TensorRT, or ONNX Runtime.
• Experience applying LLMs to document intelligence workflows, including both frontier and open-source models.
• Strong understanding of coordinate systems and spatial reasoning for absolute positioning and field detection.

Nice to have

• Familiarity with PDF parsing libraries and document preprocessing pipelines.
• Experience fine-tuning open-source models for domain-specific document tasks.
• Knowledge of evaluation metrics for document understanding tasks.

Culture & Benefits

• An honest, open culture emphasizing feedback and professional development.
• Opportunity to work from anywhere with a globally distributed team.
• 6 self-care days.
• A competitive salary.