5 months ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 15 hours agoStatus: Live

Job Summary

Lead research on multi-modal reasoning systems interpreting scientific data (images, plots, text, etc) using state-of-the-art and custom vision-language models. Design training, adaptation, and test-time strategies (instruction tuning, supervised learning, RLHF, RAG) for scientific understanding tasks; build datasets, perception modules (OCR, table/structure recognition, plot parsing), and collaborate to scale research into production-grade systems.

Location: Cambridge

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•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.

Pay and Benefits

Salary: USD 176,000 - 304,000 annually

Equity and Bonus:Equity

Perks:Annual BonusEquity

Key Requirements

•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).

Experience:Machine learningMultimodal MLScientific data

Skills:CommunicationCollaborationProblem-solving

Languages:English

Tech Stack:PyTorchHuggingfaceOCRPlot parsingRLHFRAG

Company Brief

Lila Sciences

Industry: Biotech

Website

Loading job details...

Preparing the role view and application actions.

Lila Sciences

5 months ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 15 hours agoStatus: Live

Job Summary

Location: Cambridge

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Key Responsibilities

•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.

Pay and Benefits

Salary: USD 176,000 - 304,000 annually

Equity and Bonus:Equity

Perks:Annual BonusEquity

Key Requirements

•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).

Experience:Machine learningMultimodal MLScientific data

Skills:CommunicationCollaborationProblem-solving

Languages:English

Tech Stack:PyTorchHuggingfaceOCRPlot parsingRLHFRAG

Company Brief

Lila Sciences

Industry: Biotech

Website