What we are looking for We are seeking a driven, AI-first Software Engineer to design and scale our AI platforms—the integrated computational and robotic systems that power engineering biology. You will design and ship services, pipelines, interfaces, and infrastructure that enable scientists and AI agents to co-drive research at pace, collaborating with wet-lab scientists, computational biologists, and platform engineers. This is an in-person role at either Watertown, MA, USA or London, UK.

Responsibilities - Design, build, and own full-stack systems powering our AI platforms: backend services, data pipelines, frontends, and the cloud infrastructure (AWS and related) behind them, from deployment and observability to compute orchestration for interactive and batch workloads. - Build APIs, tool servers (e.g., MCP), and integrations that connect LLM agents to lab data systems, instruments, and scientific databases. - Apply an AI-first mindset: prototype with coding agents, integrate LLM-based features where they add real leverage, and benchmark and harden agentic workflows, iterating on prompts, tools, and scaffolding for reliability. - Bring engineering rigor to architecture decisions spanning agents, data, infrastructure, and UX, and write clean, well-tested, well-documented code.

What we are looking for We are seeking a driven, AI-first Software Engineer to design and scale our AI platforms—the integrated computational and robotic systems that power engineering biology. You will design and ship services, pipelines, interfaces, and infrastructure that enable scientists and AI agents to co-drive research at pace, collaborating with wet-lab scientists, computational biologists, and platform engineers. This is an in-person role at either Watertown, MA, USA or London, UK.

Responsibilities - Design, build, and own full-stack systems powering our AI platforms: backend services, data pipelines, frontends, and the cloud infrastructure (AWS and related) behind them, from deployment and observability to compute orchestration for interactive and batch workloads. - Build APIs, tool servers (e.g., MCP), and integrations that connect LLM agents to lab data systems, instruments, and scientific databases. - Apply an AI-first mindset: prototype with coding agents, integrate LLM-based features where they add real leverage, and benchmark and harden agentic workflows, iterating on prompts, tools, and scaffolding for reliability. - Bring engineering rigor to architecture decisions spanning agents, data, infrastructure, and UX, and write clean, well-tested, well-documented code.