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

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

Offered From the posting source checked Jun 20, 2026

Verification

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Salary

$176K-$304K From the posting source checked Jun 20, 2026

401(k) match

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Market context

U.S. role benchmark (BLS OEWS)

$111,944 U.S. median for this role

Projected growth (BLS Employment Projections)

+13.7% - Much faster than average

114% above the BLS role benchmark for data and ml aggregate.

Matched to SOC 15-1252 - Data and ML aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Data From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Company

Equity

Offered From the posting source checked Jun 20, 2026

Application

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Machine Learning Scientist I/II, Multi-Modal Scientific Reasonings Cambridge, MA USA Your Impact at LILA 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. What You'll Need to Succeed - 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. Bonus Points For - Experience with scientific data modalities in real-world

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