Member of Technical Staff, Applied Research
Remote • San Francisco • FullTime
Posted 2mo ago
Job Location
San Francisco
Tech Stack
Remote Work Policy
Fully remote
Employment Type
FullTime
Categories
AI Research Engineer
About the job
We are seeking an AI Research Engineer to join our document understanding team, bridging the gap between applied research and robust engineering. In this role, you will focus on vision-language models, document processing, data curation, synthetic data generation, benchmarking, and model training and fine-tuning. The primary objective is to enhance the accuracy, speed, and cost-effectiveness of our document AI systems in production. This position requires a passion for cutting-edge AI research coupled with a strong drive for practical product impact, involving rapid prototyping, rigorous evaluation, and the transition of promising approaches into production systems.
Responsibilities
- Develop and train vision-language models for document processing and understanding.
- Build data pipelines for curation, synthetic data generation, labeling, and benchmark creation.
- Evaluate base models and perform post-training or fine-tuning to meet performance targets.
- Improve model accuracy, latency, and cost-effectiveness for real-world document workflows.
- Design and maintain benchmarks for extraction quality, layout understanding, OCR performance, reasoning accuracy, and system reliability.
- Work with diverse real-world documents including PDFs, scanned documents, tables, charts, forms, and multi-page enterprise documents.
- Collaborate with engineering teams to deploy research prototypes into production.
- Engage with customers to translate product requirements into benchmarks, experiments, and model improvements.
- Stay abreast of the latest research in vision-language models, document AI, post-training, synthetic data, and agentic systems.
- Utilize modern AI coding workflows and tools for rapid development.
Requirements
- 3–7 years of experience in machine learning engineering, applied research, or research engineering.
- Strong ML foundation with hands-on experience in benchmarking and training models.
- Proficient Python skills and familiarity with modern ML tooling, particularly 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 for measurable performance improvements.
- Strong engineering judgment and ability to write clean, production-quality code.
- Comfort in a fast-paced startup environment with high ownership and limited structure.
- Adaptable, scrappy, and self-directed.
- Strong technical writing and communication skills.
Benefits
- Work on a core AI infrastructure problem: making complex documents understandable and actionable for AI systems.
- Build production systems at the frontier of vision-language models and document AI.
- 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.