Member of Technical Staff, Applied Research
Hybrid (San Francisco)FullTimePosted 3mo ago
About the role
AI Research Engineer role in document understanding, bridging applied research and engineering to develop vision-language models, document processing, data curation, synthetic data generation, benchmarking, training, fine-tuning, and post-training for production-ready document AI systems.
Responsibilities
- Develop and train vision-language models for document processing and document understanding.
- Build data pipelines for data curation, synthetic data generation, labeling, and benchmark creation.
- Evaluate base models and perform post-training or fine-tuning to hit specific performance targets.
- Improve model accuracy, latency, and cost-effectiveness across real-world document workflows.
- Design and maintain benchmarks to measure extraction quality, layout understanding, OCR performance, reasoning accuracy, and end-to-end system reliability.
- Work with messy real-world documents, including PDFs, scanned documents, tables, charts, forms, and multi-page enterprise documents.
- Collaborate with engineering to move successful research prototypes into production.
- Work directly with customers when needed to translate product requirements into benchmarks, experiments, and model improvements.
- Stay close to the latest research in vision-language models, document AI, post-training, synthetic data, and agentic systems.
- Use modern AI coding workflows and tools to move quickly.
Qualifications
- 3–7 years of experience in machine learning engineering, applied research, or research engineering.
- Strong ML foundation, including hands-on experience benchmarking and training models.
- Strong Python skills and comfort with modern ML tooling, especially PyTorch.
- Experience with computer vision, vision-language models, NLP, document AI, OCR, extraction, or agentic AI systems.
- Ability to build experiments, evaluate results, and iterate quickly toward measurable performance improvements.
- Strong engineering judgment and ability to write clean, production-quality code.
- Comfort working in a fast-paced startup environment with high ownership and limited structure.
- Adaptable, scrappy, and self-directed — someone who can figure things out without waiting to be told.
- Strong technical writing and communication skills.
Skills
Required:Vision-language modelsPythonPyTorchBenchmarking
Benefits
- Not explicitly listed in the description.
Last updated Oct 7, 2026. Interested? Apply on the company site.
About LlamaIndex
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