ML Research Scientist I/II, Multimodal Data Extraction

Your Impact at LILA As a ML Research Scientist - Multimodal Data Extraction , you will advance Lila’s vision of scientific superintelligence by developing foundation models that autonomously read, interpret, and structure scientific knowledge across text, images, and experimental data in the physical sciences. Your research will help unify the world’s scientific information into machine-understandable form, powering reasoning, prediction, and autonomous discovery across materials science and chemistry. What You'll Be Building Research and develop  AI systems  that extract and structure knowledge from diverse scientific sources. Design and fine-tune  large language, multi-modal and specialized models  for factual, interpretable data extraction. Build scalable pipelines for  unstructured and heterogeneous scientific data , integrating text, tables, and visuals. Collaborate with domain experts to align extracted data with real-world discovery workflows. Publish research that advances the state of the art in multimodal understanding and AI-driven knowledge extraction. What You’ll Need to Succeed PhD (or equivalent research experience) in Computer Science, Chemistry, Materials Science, or related field. Expertise in  machine learning ,  NLP , and  vision–language modeling  using  PyTorch  and  Hugging Face Transformers . Proven ability to train, fine-tune, and evaluate  LLMs and multimodal models  for scientific data extraction. Strong understanding of data structures and representations used in the physical sciences. Demonstrated research impact through publications, preprints, or open-source work (e.g., NeurIPS, ICLR, ICML, ACL, EMNLP, Scientific Journals). Bonus Points For Experience with  multimodal fusion architectures  and document-level understanding. Knowledge of  scientific document parsing  (OCR, table extraction, figure-caption linking). Familiarity with  knowledge graph construction  or reasoning systems for science. Experience with noisy or heterogeneous real-world scientific data. Collaborative mindset and passion for advancing AI in the physical sciences.   Compensation We offer competitive compensation including bonus potential and generous early equity. The final offer will reflect your unique background, expertise, and impact. Expected Base Salary Range $176,000 — $304,000 USD About LILA Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves. LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai. Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply. We’re All In Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy . A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.