Job description

The Role 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. You will: • 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 What We Offer • 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

Requirements

Must-haves

This is an in-person role in Cambridge, MA. You will:
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
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

Nice-to-haves

5+ years of experience developing deep learning models; prior experience in generative modeling, protein/biomolecular ML, or large-scale sequence modeling is a plus

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlpython

Platform: gem
Effort: medium