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About

At Nabla Bio we develop integrated AI and wet-lab technologies to design protein drugs. We’ve partnered with pharmaceutical giants like AstraZeneca, Bristol Myers Squibb, and Takeda to create the next generation of life-changing therapeutics. We're a well-funded, revenue-generating company with a team of both dry- and wet-lab scientists committed to developing the best protein design technologies to make drug development easier and more successful. Join us in our fully in-person environment where research, engineering, and solving therapeutic problems is at the heart of everything we do.

The role

Nabla Bio is at an exciting stage of growth as we expand our partnerships with leading pharma companies to create world-changing protein therapeutics. Our team has developed new computational and experimental technologies that are being used across several drug design campaigns, and we're investing heavily in further research to improve them. A key driver of our progress is investing in our computational infrastructure to decrease R&D cycle times and streamline application of developed technologies. We’re looking for a talented engineer to make this happen. You’ll be instrumental in:

• Designing, building, and maintaining infrastructure for data management, large-scale training, rapid experimentation, and deploying our ML models.
• Creating and managing systems for distributed inference of biomolecular ML models for protein design.
• Streamlining, deploying, and supporting essential computational workflows for our scientists.
• Collaborating with both our wet- and dry-lab scientists to identify and implement infrastructure improvements that increase efficiency and outcomes.

Qualifications

• Proven experience in designing and building scalable computational research and ML systems that enable a quick transition from rapid prototyping to reliable deployment.
• 2+ years of industry experience in machine learning infrastructure, pipeline building, distributed training and inference, and deployment
• Strong software design skills, with an ability to identify the right abstractions and balance between flexibility and complexity.
• Experience scaling infrastructure in a fast-growing startup or team.
• Expertise in deploying and managing automation (e.g. CI), development environments (e.g. Docker, AWS), and version control systems (GitHub) for ML teams.