Research Engineer

Role Overview

We’re looking for a Research Engineer to work across our open‑source repositories, inference API, and model training stack. You’ll operate at the intersection of applied research and engineering — shaping the models that power real‑world document intelligence systems used by enterprises and developers globally.

You will be training and evaluating new model architectures, integrating them into production, and shipping updates across our open‑source ecosystem. You’ll also help close the loop with users — investigating issues, improving benchmarks, and turning real feedback into better model performance.

Our team focuses on training small, efficient models that outperform much larger LLMs on domain‑specific tasks such as OCR, structured extraction, and math recognition. We move fast, prioritize practical results, and build tools that are open, reproducible, and built to last.

Responsibilities

• Train and evaluate task‑specific models (OCR, layout, text recognition, extraction); explore architectures and training strategies to optimize performance.
• Optimize inference performance and profile across hardware setups (H100s, L40s, CPUs).
• Contribute to open‑source repositories by shipping features and improvements to model APIs, data loaders, evaluation scripts, and benchmark tooling.
• Build and maintain datasets for supervised and synthetic training; create reproducible pipelines for data versioning and evaluation.
• Run experiments, track metrics, and publish findings that inform model design and internal research direction.
• Engage with users and partners, occasionally joining calls or Slack threads to help customers evaluate, deploy, and extend models.

Ideal Candidate

You have shipped models that made it into production and understand how to balance exploration with delivery, turning research insights into products people use. You work autonomously in unstructured environments and are a strong collaborator, communicating clearly and documenting your work.

Required qualifications

• 3+ years experience training, fine‑tuning, and evaluating LLMs.
• Trained at least one production‑grade model used in real‑world applications.
• Deep expertise in PyTorch and Python, with strong fundamentals in deep learning.
• Comfortable with data engineering, benchmarking, and performance profiling across hardware setups.

Bonus Points

• Experience with OCR, document AI, or structured extraction.
• Published work such as papers, benchmark reports, or technical blog posts.
• Major contributions to open‑source projects, especially in ML, vision, or NLP.
• Enjoy writing about your work and sharing learnings with the community.