Member of Technical Staff, Applied Research

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.

Requirements

• 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.

Nice-to-Haves

• Prior startup experience, especially at an early-stage or high-growth AI company.
• Experience as a founder or early startup engineer.
• Experience building or improving document processing systems.
• 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.