AI Platform Engineer

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, Type Script, 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