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Job description
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TL;DR
AI Research Engineer (Document AI): Developing and training vision-language models for document processing with an accent on data curation, synthetic data generation, and benchmarking. Focus on building production-ready systems that improve model accuracy, latency, and cost-effectiveness for real-world document workflows.
Location: Must be based in or able to work from San Francisco (Hybrid)
Salary: $180,000 – $250,000
Company
A fast-growing AI startup focused on core infrastructure for document understanding and agentic systems.
What you will do
- Develop and train vision-language models for document understanding.
- Build data pipelines for curation, synthetic data generation, and benchmarking.
- Perform post-training and fine-tuning to meet performance targets.
- Optimize model accuracy, latency, and cost-effectiveness for production.
- Design benchmarks to measure extraction quality and system reliability.
- Collaborate with engineering to transition research prototypes into production systems.
Requirements
- 3–7 years of experience in machine learning engineering or applied research.
- Strong foundation in benchmarking and training models.
- Proficiency in Python and PyTorch.
- Experience with computer vision, vision-language models, NLP, or OCR.
- Ability to write clean, production-quality code.
- Must be comfortable working in a fast-paced startup environment.
Nice to have
- Prior experience in early-stage or high-growth AI companies.
- Experience with synthetic data generation and post-training workflows.
- Familiarity with modern AI tooling like vLLM, Pydantic, or Cursor.
- Experience building document processing systems.
Culture & Benefits
- Work on core AI infrastructure with high technical ownership.
- Collaborate directly with technical founders and an ambitious engineering team.
- Join a company with strong open-source adoption and commercial traction.
- Opportunity to influence product capability and technical direction.
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