Scholar
Sean M. Hendryx
Google Scholar ID: BFyfAV4AAAAJ
Scale AI
Citations & Impact
Citations
371
H-index
9
i10-index
9
Publications
20
Co-authors
9
list available
Publications
Resume (English only)
Academic Achievements
- Led teams to publish at top-tier conferences, including Scale AI’s first main-track NeurIPS paper.
- Released industry-leading benchmarks.
- Published and presented select works including:
- - 'Aligning LLMs with Representation Learning' (Scale AI Webinar 2024)
- - 'Bringing Foundation Models to Automotive Data Engines' (Tech.AD Europe 2024)
- - 'A Baseline Analysis of Reward Models’ Ability To Accurately Analyze Foundation Models Under Distribution Shift' (AAAI ReLM 2024 Workshop)
- - 'Federated Reconnaissance: A New Framework for Distributed Continual Learning' (NAML 2021)
- - 'Meta-Learning Initializations for Image Segmentation' (NeurIPS 2020 Meta-Learning Workshop)
- - 'Continual Progress in Cascading Model Systems by Ensuring Reproducible State' (Keynote, Deep Learning World 2020)
- - 'Balancing Machine Learning Innovation and Scalability in Fast Growing Startups' (Guest lecture, UCSB, Winter 2021)
Research Experience
- Currently leads the Reasoning & Agents Research team at Scale AI, working on RL, reasoning, agents, and alignment.
- Previously led applied ML for Scale AI’s GenAI data engine, building agentic services, training in-house LLMs for quality control and efficiency, and developing fraud/spam/cheating detection systems.
- Created AFM-1, Scale’s vision foundation model.
- Researched and developed deep learning systems at Standard Cognition, including large-scale video training, action recognition, training automation, transfer learning, domain adaptation, metrics development, hard mining, and model robustness.
- Led migration of the core human pose estimation stack from TensorFlow 1 to PyTorch and implemented a real-time video inference service using TorchScript, Rust, and GStreamer.
- Was the first engineer at Explorer AI, an autonomous vehicle mapping startup later acquired by Standard Cognition.
