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AI Text-to-Table Synthesis Engineer

Savannah Watanabe

Full-time · Job · Berlin

About the role

Stack Overflow asked 90,000 developers which data format they spend the most time wrangling. Tables won by a wide margin. Clinical trial results buried in PDF prose. Financial metrics scattered across earnings call transcripts. Regulatory filings where the important numbers are described in sentences, not cells. We build the systems that extract structured tables from unstructured text — and we're looking for someone to push those systems to the next level. The challenge is generalization. Extracting a simple two-column table from a clean PDF is solved. Reconstructing a complex nested table from a multi-paragraph narrative description — where the structure is implied, not explicit — is genuinely hard. You'll design models that handle the long tail: irregular structures, cross-references, units buried in footnotes, and tables that span multiple document sections. We publish research, attend ACL and EMNLP, and encourage the team to pursue open problems. This role is 70% engineering and 30% research. You'll mentor two mid-level engineers and report to our Chief Scientist.

Responsibilities

  • Design models for complex table extraction and reconstruction from unstructured text
  • Handle irregular table structures, cross-references, and multi-section spans
  • Build training pipelines for table structure inference from narrative descriptions
  • Develop evaluation benchmarks covering the long tail of table complexity
  • Publish findings at NLP conferences and contribute to internal research agenda
  • Mentor two mid-level engineers on table understanding techniques

Requirements

  • 6+ years in NLP or document understanding research with publications
  • Deep expertise in transformer-based architectures for structured prediction
  • Experience with table extraction, information extraction, or document AI
  • Strong PyTorch skills and familiarity with HuggingFace ecosystem
  • Publication record at ACL, EMNLP, NAACL, or equivalent venues

Benefits

  • Fully remote across European time zones
  • Conference budget with no annual cap
  • 30 days PTO plus local holidays
  • Equity participation
  • Home office equipment fund of €3,000

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Categories

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch hugging face docker aws

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$160k – $200k / year

AI Expertise

AI & Machine Learning Engineers data science & analytics

Skills

python pytorch hugging face docker aws statistics

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