← All jobs
AI Platform Engineer
OnsiteSeniorFull-time
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 have forged significant partnerships with leading pharma companies. The Role We’re hiring a senior engineer to build core infrastructure and tools that connect our AI models to real-world use. You’ll work across the full stack — front-end, back-end, and ML infrastructure — to build systems that close the gap between idea and implementation for our AI and wet-lab scientists. Your work will enable faster design cycles, tighter feedback loops, and more seamless collaboration across disciplines. This is a high-impact, hands-on role that directly supports our scientists, collaborators, and pharma partners. You’ll build APIs, dashboards, training and inference pipelines that turn frontier AI models into production-grade tools for drug design. Another focus will be accelerating scientific workflows with LLMs — building systems where AI helps propose and learn from experiments to drive faster cycles of discovery. It’s a rare opportunity to engineer this loop with real-world, large-scale experimental feedback (1 million drug designs measured every month). 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: Build and maintain user-facing applications (UIs, APIs, and dashboards) that reduce the idea-to-implementation gap in drug design workflows Develop robust ML training and inference systems to accelerate experimentation during model development and expose our models through scalable back-end services Collaborate closely with scientists to understand pain points and ship high-leverage tools, including AI-augmented experiment planning and analysis Own full-stack development from prototype to product
Skills
ML InfrastructureFull StackAPIsDashboardsTraining PipelinesInference PipelinesLLMsAI Models