AI Scientist

About the role

Nabla Bio is building AI to design new medicines. We combine cutting-edge ML with fast, human-relevant lab validation to create biomolecules on demand. This lets us go after hard diseases and build new drug formats that traditional approaches can’t reach. We’re backed by top investors like Radical and Khosla Ventures and work with partners including AstraZeneca, Takeda, and Bristol Myers Squibb. We’re hiring an exceptional AI Scientist to lead development of our core biomolecular modeling technologies. You’ll be responsible for building and improving the foundation models that power Nabla’s therapeutic design capabilities — from architecture design and training to experimental validation. This is a rare opportunity to do AI research with real-world, large-scale experimental feedback: your models will be tested not just with loss curves and benchmarks, but in wet-lab assays measuring therapeutic function, safety, and precision. Our platform enables you to test dozens of modeling hypotheses in parallel, with experimental results across a million drug designs returned in just a few weeks. See our papers for examples of our work [1][2], and their coverage in Science Magazine and Endpoints News. This is an in-person role in Cambridge, MA.

Responsibilities

• Design, implement, and evaluate new training data, model architectures, training schemes, and loss functions for biomolecular generation and prediction
• Drive major improvements in generative and predictive performance based on experimental feedback
• Collaborate with AI engineers to productionize models for use in internal and pharma partner design workflows
• Stay on top of the state of the art in ML, protein modeling, and sequence design—and push it forward

Qualifications

• 5+ years of experience developing deep learning models; prior experience in generative modeling, protein/biomolecular ML, or large-scale sequence modeling is a plus
• Strong engineering fluency in Python and PyTorch
• Experience with distributed training and scaling large models in HPC/cloud environments
• Track record of creativity, rigor, and technical leadership in ML research
• Comfort working closely with experimentalists to connect model behavior to real biological outcomes

Benefits

• The ability to test and validate ML hypotheses using one of the most powerful experimental platforms in biotech
• A chance to shape foundational modeling capabilities for programmable drug design
• Close collaboration with experts in wet-lab biology, bioinformatics, and software engineering
• A focused, technically ambitious team solving hard problems end-to-end
• Highly competitive salary, equity, and benefits package