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

3 мес. назад

USAMiddle

machine learningvision-language modelsdata benchmarksocrreinforcement learningscientific data

Lead research and develop multi-modal reasoning systems using vision-language models for scientific data interpretation.

Будет плюсом

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.

Условия

We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.
International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.

Другое

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.
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.
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.
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
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.
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.
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