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Open nowPosted 8 days ago

AI Engineer

Workable (global search)108,016 open roles

Where
Chicago, IL, United States
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Your applicationOpen nowAI EngineerWorkable (global search) · Chicago, IL, United States

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The clock on this job

Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 7 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 8 days ago

Workable (global search) median: 7 days open

The posting

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟭𝟬𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟮𝟬𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟭𝟬𝟬-𝟮𝟬𝟬 𝗟𝗣𝗔)

Experience: 3+ yrs

Location: Chicago, Illinois, United States

Job Type: Full-time

We are looking for an experienced AI Engineer to design and build the intelligence layer across a document-to-return workflow. The role focuses on developing production-grade AI systems that transform complex financial and tax documents into reliable, structured data and support tax professionals in identifying missing information, inconsistencies, and potential errors.

This is a hands-on engineering role focused on document intelligence, LLMs, extraction, agentic systems, evaluation, and human-in-the-loop workflows. The ideal candidate combines strong technical skills with a high bar for accuracy, traceability, observability, and production reliability.

Requirements

Key Responsibilities

  • Build production systems for document classification, OCR, parsing, and structured data extraction.
  • Process PDFs, scanned documents, tax forms, financial statements, receipts, and other unstructured financial information.
  • Design extraction workflows that preserve source context, handle ambiguity, and route low-confidence results for human review.
  • Develop LLM-powered document understanding and intelligent extraction capabilities.
  • Build AI agents that analyze completed returns against source documents and relevant tax context.
  • Identify missing information, inconsistencies, potential errors, and other issues and present findings clearly for professional review.
  • Design human-in-the-loop workflows that provide appropriate confidence signals, source citations, review controls, and correction mechanisms.
  • Build scalable evaluation frameworks for structured and unstructured document-processing systems.
  • Define evaluation datasets, ground-truth labels, scoring methodologies, benchmarks, and statistical analysis approaches.
  • Establish observability and feedback systems to measure extraction quality, model performance, and user outcomes.
  • Monitor production AI workflows and continuously improve accuracy, reliability, and coverage.
  • Identify high-effort manual steps within document and return-preparation workflows where AI can provide measurable value.
  • Collaborate with engineering and domain experts to translate real-world workflow requirements into reliable AI systems.
  • Establish reproducible testing and evaluation processes for models, agents, and extraction pipelines.
  • Contribute to expanding AI capabilities across document processing, return review, and professional workflows.

What Makes You a Great Fit

  • 3+ years of experience building production AI, machine learning, or intelligent automation systems.
  • Strong hands-on experience with document OCR, document understanding, parsing, and structured data extraction.
  • Experience working with PDFs, forms, scanned documents, financial documents, or other complex unstructured data.
  • Strong understanding of LLM-based document processing and modern AI techniques for extraction and reasoning.
  • Experience designing and implementing evaluation frameworks for AI or machine-learning systems.
  • Strong knowledge of evaluation datasets, ground-truth labeling, scoring methodologies, benchmarking, and statistical analysis.
  • Experience building or working with agentic AI systems and human-in-the-loop workflows.
  • Strong understanding of observability, reproducibility, model monitoring, and production AI reliability.
  • Proficiency in Python and experience building scalable production systems.
  • Strong analytical and problem-solving skills with exceptional attention to accuracy and detail.
  • Ability to design AI workflows where outputs are traceable, auditable, explainable, and actionable.
  • Strong product and engineering judgment with a practical, outcome-oriented approach to AI development.
  • Comfortable working closely with domain experts and incorporating real-world feedback into AI systems.
  • Strong bias toward shipping, experimentation, measurement, and continuous improvement.
  • Comfortable operating in a small, high-ownership environment where engineering and product responsibilities are closely connected.
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