Machine Learning Scientist I/ii, Multi-modal Scientific Reasonings

Lila Sciences Lila Sciences · AI Frontier · Alewife, Cambridge, MA · Physical Sciences AI

Research role focused on advancing multi-modal reasoning with vision-language models (VLMs) on scientific data, including figures, plots, and microscopy. The role involves designing and building state-of-the-art methods for scientific understanding tasks, developing perception modules, and creating datasets and benchmarks. Collaboration with domain scientists and engineers to scale research into production systems is key.

What you'd actually do

  1. Lead research on multi‑modal reasoning systems that interpret scientific data (images, plots, text, etc) using state‑of‑the‑art and custom VLMs.
  2. Design training, adaptation and test-time methods and strategies (e.g., instruction tuning, supervised learning, RLHF, RAG) for scientific understanding tasks.
  3. Build datasets and benchmarks from real scientific artifacts (e.g., microscopy, spectra, protocols) to understand model performance.
  4. Develop perception modules (e.g, OCR, table/structure recognition, plot parsing) for multi-modal data modalities.
  5. Collaborate with domain scientists and engineers to scale research into production ready systems for scientific superintelligence.

Skills

Required

  • 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.

Nice to have

  • 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.

What the JD emphasized

  • 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.

Other signals

  • multi-modal reasoning
  • vision-language models
  • scientific data interpretation
  • custom VLMs
  • scientific superintelligence