Lila Sciences
Machine Learning Scientist I/II, Multi-Modal Scientific Reasonings
About this role
Lila Sciences is seeking a Machine Learning Scientist to enhance multi-modal reasoning using vision-language models on scientific data. The role involves developing advanced methods and collaborating with domain experts to create scalable systems for Scientific Superintelligence.
What you'll do
- Lead research on multi-modal reasoning systems for scientific data interpretation
- Design training and evaluation methods for scientific understanding tasks
- Build datasets and benchmarks from real scientific artifacts
- Develop perception modules for multi-modal data processing
- Collaborate with scientists to transition research into production systems
What they're looking for
- Advanced degree in CS/AI, Applied Math/Stats, or physical sciences
- Experience with multi-modal machine learning and vision-language models
- Knowledge of scientific quality assurance and benchmark design
- Proficiency in multi-modal fine-tuning and document understanding
- Strong skills in modern machine learning frameworks like PyTorch
- Clear communication and collaboration skills
Benefits
- Competitive base compensation and bonus potential
- Generous early-stage equity
- Medical, dental, and vision coverage
- Flexible time off with company-wide holidays
- Paid parental leave
- Educational assistance program
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Lila Sciences
Lila Sciences builds AI and automation systems for scientific research, including tools for automated analysis, control systems for lab operations, and large language models for scientific tasks. The company is hiring software engineers, machine learning scientists, research engineers, and automation specialists to develop and maintain these scientific computing platforms.
View all jobs at Lila SciencesLikely interview questions
- Walk us through a multi-modal ML project you've shipped or published—how did you approach combining vision and language modalities, and what were the key challenges?
- Describe your experience fine-tuning vision-language models. What training strategies (instruction tuning, RLHF, etc.) have you found most effective, and why?