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

- **Company:** Lila Sciences, Inc.
- **Location:** Cambridge, United States
- **Salary:** $176,000.0 - $304,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Machine Learning, Language Modeling, Open Source Technology, Tensorflow, Supervised Learning, Pytorch, HuggingFace
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/machine-learning-scientist-i-ii-multi-modal-scientific-reasonings-lila-sciences-7844893

## About the Role

* 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., * 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.

## Description

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.


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