- 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
- 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
- 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
Top Skills
What We Do
We develop AI and wet-lab technologies that enable the rational design of developable, selective, and functional drugs against previously undruggable targets. To maximize the patient impact of our platform, we collaborate with leading pharmaceutical companies, including AstraZeneca, Bristol Myers Squibb and Takeda, to expand their pipelines with high-quality drug candidates. If successful we could double the number of disease-relevant drug targets the industry advances for drug development. Since launching in 2021 we have raised $37 million with the backing of leading investors, including Radical Ventures, Khosla Ventures and Zetta Venture Partners. Our technical team is based in Cambridge, Massachusetts and continues to recruit top machine learning and synthetic biology talent. For more information check out the Careers section of our website at www.nabla.bio.
