Job description

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 Qualifications • 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 What We Offer • 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

Requirements

Must-haves

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

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlreacttypescriptpythonkubernetesdocker

Platform: gem
Effort: medium