Machine Learning Scientist I/II, Multi-Modal Scientific Reasonings
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Lilasciences · Cambridge, UKPrepare for this interview
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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 laboratories such as microscopy images. Publications in top ML/CV/NLP venues or tangible impact in applied industrial research. Contributions to open‑source multi‑modal tooling, evaluation suites, or datasets.
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