Machine Learning Scientist (AI)

TL;DR

Machine Learning Scientist (AI/VLM): Advance multi-modal reasoning with vision-language models (VLMs) on real-world scientific data including figures, plots, and microscopy, with an accent on designing and building state-of-the-art methods. Focus on leading research on multi-modal reasoning systems, developing perception modules, and scaling research into production-ready scientific superintelligence systems.

Location: Onsite in Cambridge, MA, USA

Compensation: $176,000–$304,000 USD per year

Company

hirify.global is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science, pioneering a new age of boundless discovery by applying AI to the scientific method.

What you will do

• Lead research on multi-modal reasoning systems that interpret scientific data using state-of-the-art and custom VLMs.
• Design training, adaptation, and test-time methods and strategies (e.g., instruction tuning, 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.

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 contributions.
• 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.

Nice to have

• 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.

Culture & Benefits

• Bonus potential and generous early equity.
• Commitment 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.
• Pioneering scientific superintelligence to solve humankind's greatest challenges in human health, climate, and sustainability.