Ankur Goyal is a seasoned founder and engineering leader with 15 years of experience building data-intensive systems and AI products from the ground up in the San Francisco Bay Area. He co-founded and scaled Impira through acquisition by Figma, briefly led ML Platform at Figma, and now runs Braintrust as Founder & CEO, blending product vision with hands-on technical leadership. His contributions to high-profile open-source projects—such as TensorFlow-centric work on Hugging Face Transformers and core enhancements to DuckDB—show a rare mix of ML model integration and database internals expertise. Comfortable across full-stack development, distributed databases, and production ML pipelines, he pairs strategic company-building experience with deep implementation skills. Ankur’s background from Carnegie Mellon and track record of shipping both infrastructure and applied ML tools make him particularly effective at turning research-grade models into scalable, customer-facing systems.
15 years of coding experience
11 years of employment as a software developer
BS CS, Computer Science, BS CS, Computer Science at Carnegie Mellon University
Contributions:41 reviews, 75 commits, 23 PRs in 2 months
Contributions summary:Ankur primarily focused on setting up the foundational structure and initial components of the project. Their contributions included the creation of the `setup.py` file, basic CLI functionality with `__main__.py`, and the introduction of the `src/docqa/version.py` file. Furthermore, they implemented the integration of external libraries, including a remote pipeline and model, and added the basic structure for document processing.
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
Role in this project:
ML Engineer
Contributions:73 reviews, 11 commits, 12 PRs in 2 months
Contributions summary:Ankur primarily contributed to the development and enhancement of the `transformers` library, which focuses on state-of-the-art machine learning for NLP tasks. Their work included implementing and refining models like `LayoutLMForQuestionAnswering` within the TF environment, indicating a focus on TensorFlow integration. The user added support for the `tensorflow-aarch64` platform, and they also added the DocumentQuestionAnswering pipeline. Their contributions involved extensive code modifications, test case additions, and documentation updates, suggesting an active role in model development and pipeline integration.
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