Machine Learning Scientist I/II,… at Lila Sciences · Cambridge

Summary: Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. They are seeking a Machine Learning Scientist to lead research on multi-modal reasoning systems that interpret scientific data using state-of-the-art vision-language models. Responsibilities: - 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 Required Qualifications: - 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 Preferred Qualifications: - 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 Required Skills: Multi-modal ML, Vision-language models, Machine learning frameworks Important Skills: Dataset curation Benefits: Competitive compensation including bonus potential and generous early equity