LlamaIndex

Member of Technical Staff, Applied Research

San Francisco · Staff+

Sponsorship not specifiedDetected 14 days ago

PythonMachine LearningData EngineeringResearchCommunicationWritingAdaptability

About the role

This role is ideal for someone who sits between applied research and strong engineering.
You should be excited by frontier AI work, but equally motivated by practical product impact.
This is not a pure research role where ideas stay in papers.

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.

Strong ML foundation, including hands-on experience benchmarking and training models.
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.

Prior startup experience, especially at an early-stage or high-growth AI company.
Experience as a founder or early startup engineer.
Experience with synthetic data generation, post-training, fine-tuning, or benchmark design.
Familiarity with tools such as vLLM, Pydantic, uv, ruff, mypy, Claude Code, Cursor, or similar modern AI engineering workflows.
Experience with open-source AI infrastructure or developer tools.
Join a fast-growing startup with strong open-source adoption and commercial traction.
Work directly with technical founders and a highly ambitious engineering team.
Have real ownership over model quality, product capability, and technical direction.

Build production systems at the frontier of vision-language models and document AI.
You will work on vision-language models, document processing, data curation, synthetic data generation, benchmarking, training, fine-tuning, and post-training.
Develop and train vision-language models for document processing and document understanding.
Stay close to the latest research in vision-language models, document AI, post-training, synthetic data, and agentic systems.
3-7 years of experience in machine learning engineering, applied research, or research engineering.

make our document AI systems more accurate, faster, and more cost-effective in production.
You will be expected to prototype quickly, evaluate rigorously, and help turn promising approaches into production systems used by customers.
We are looking for an AI Research Engineer to join our document understanding team.
Work directly with customers when needed to translate product requirements into benchmarks, experiments, and model improvements.

This listing is sourced directly from LlamaIndex's careers page and normalized into a canonical job model.