Scientist I/II, Foundation Models (Life Sciences)

TL;DR

Scientist I/II (Foundation Models): Researching and developing large-scale generative models and reasoning frameworks for automated scientific discovery in life sciences with an accent on biological sequences and molecular structures. Focus on designing and training models within a closed-loop discovery engine to solve complex medical problems.

Location: San Francisco, CA USA

Salary: $176,000 - $304,000 USD

Company

hirify.global is building Scientific Superintelligence to solve humankind's greatest challenges in medicine, materials, and energy.

What you will do

• Contribute to research on foundation models for biological sequence design, structure prediction, and multimodal scientific reasoning.
• Design, train, and evaluate generative models on biological and chemical data using domain-specific constraints.
• Implement the end-to-end ML process within the "Lab-in-the-Loop" lifecycle, designing feedback loops where experimental results improve models.
• Translate biological questions into well-defined ML problems in collaboration with wet-lab scientists.
• Maintain high research quality and methodology standards within the foundation models program.

Requirements

• PhD in Computer Science, Machine Learning, Computational Biology, or related quantitative field (or Master's with equivalent research experience).
• Strong foundation in generative model architectures and hands-on experience in model development.
• Ability to formulate and execute research independently from problem definition through experimentation.
• Familiarity with at least one life science domain such as molecular biology, genomics, or protein engineering.
• Experience collaborating with experimental scientists or working with biological/chemical data.
• Proficiency in ML frameworks (PyTorch, JAX, or TensorFlow) and GPU-based training workflows.

Nice to have

• Experience in computational protein design or molecular structure prediction.
• Experience with active learning loops or closed-loop experimental workflows.
• Contributions to open-source ML tools, frameworks, or scientific benchmark datasets.
• High-impact publications in venues like NeurIPS, ICML, ICLR, AAAAI, or Nature journals.

Culture & Benefits

• Competitive base compensation with bonus potential and generous early-stage equity.
• Comprehensive benefits packages for both US and International employees.
• Fast-paced startup environment driven by values of truth, trust, curiosity, grit, and velocity.
• Opportunity to tackle problems of historic importance in the frontier of AI for Science.