What are the responsibilities and job description for the AI Platform Engineer position at Nabla Bio?
Boston, MA
Dry Lab
In office
Full-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 work with partners including AstraZeneca, Takeda, and Bristol Myers Squibb.
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:
Req ID: R3
Dry Lab
In office
Full-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 work with partners including AstraZeneca, Takeda, and Bristol Myers Squibb.
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 production, including deployment and observability
- Leverage modern AI dev tooling to move fast and stay focused on high-value work
- 5 years of experience as a full-stack, ML infra, or platform engineer
- Experience building backend systems that serve ML models in production
- Strong frontend development skills (React, TypeScript, etc.)
- Deep fluency in Python; familiarity with cloud-native tools and containerization (Kubernetes, Docker)
- Strong product taste, sense, and ability to partner with wet-lab scientists and AI researchers
- High agency and a track record of shipping quickly with quality
- A fast-moving environment where you can build and ship tools that impact real drug programs
- The opportunity to shape how cutting-edge AI and LLMs are used to accelerate scientific discovery
- Access to modern ML models and wet-lab infrastructure for rapid experimentation
- A small, focused team where your engineering decisions have outsized impact
- Highly competitive salary, equity, and benefits package
Req ID: R3