Machine Learning Scientist I/II, Multi-Modal Scientific Reasonings

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About this role

Machine Learning Scientist I/II, Multi-Modal Scientific Reasonings Cambridge, MA USA Your Impact at LILA We're hiring a Machine Learning Scientist to advance multi‑modal reasoning with vision‑language models (VLMs) on real-world scientific data including, but not limited to: figures and plots, microscopy data from diverse sources. You'll design and build state‑of‑the‑art methods to advance the state of Scientific Superintelligence. What You'll Be Building - Lead research on multi‑modal reasoning systems that interpret scientific data (images, plots, text, etc) using state‑of‑the‑art and custom VLMs. - Design training, adaptation and test-time methods and strategies (e.g., instruction tuning, supervised learning, RLHF, RAG) for scientific understanding tasks. - Build datasets and benchmarks from real scientific artifacts (e.g., microscopy, spectra, protocols) to understand model performance. - Develop perception modules (e.g, OCR, table/structure recognition, plot parsing) for multi-modal data modalities. - Collaborate with domain scientists and engineers to scale research into production ready systems for scientific superintelligence. What You'll Need to Succeed - Advanced degree in a relevant field (CS/AI, Applied Math/Stats, EE) or a physical‑sciences discipline (Materials, Chemistry, Physics) with strong ML focus; or equivalent research/industry experience. - Track record in multi‑modal ML or VLMs demonstrated via shipped systems, publications, or open‑source. - Understanding of scientific QA/benchmarks and custom evaluation design. - Experience with multi-modal fine-tuning, document parsing & understanding, dataset curation and benchmarking. - Strong engineering skills centered on modern machine learning frameworks (e.g., PyTorch, Huggingface). - Clear communication and collaboration in cross‑functional settings. Bonus Points For - Experience with scientific data modalities in real-world

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