Software Engineer, AI Platforms

Cultivarium•London, England

54d•Onsite

About The Position

Cultivarium builds scientific tools that turn biological discovery into real-world capability. As a Frontier Research Contractor, we provide technical solutions and hands-on support to partners working on the problems of today. Born a Focused Research Organization, we are drawn to the hardest technological problems in biology and built to tackle them with intensity. We prize builders who efficiently leverage resources to deliver tangible solutions at the frontier, for outsized societal benefit. Our ambition is to study and engineer non-model organisms across the entire tree of life. We're looking for a driven, creative engineer with an AI-first mindset to build and scale our AI platforms, the integrated computational and robotic systems that power engineering biology at Cultivarium. You'll design and ship the services, pipelines, interfaces, and infrastructure that let scientists and AI agents co-drive research at pace, working closely with wet-lab scientists, computational biologists, and platform engineers. A successful candidate sits at the intersection of AI systems and production software engineering, brings strong judgment to ambiguous problems, and raises the bar for how AI tools are woven into the day-to-day work of a modern biology lab. You'll create impact by building durable software foundations for an agentic research platform and bringing engineering rigor to open-ended problems. This is an in-person role at either our Watertown, MA USA or London, UK location.

Requirements

You believe that AI can be transformative not just for thinking but for productivity, and you proactively seek out and build new tooling to keep Cultivarium at the cutting edge of what's possible.
Authorized to work without sponsorship.
Bachelor's in computer science, software engineering, or a related field, or equivalent professional experience.
3–7+ years shipping production systems. We care more about evidence of compelling outputs and high velocity than years on a résumé. Show us what you've built and how fast you built it.
Strong proficiency in a modern backend language (e.g. Python, Go) and comfort across the stack, including frontend frameworks (e.g. React, Next.js).
Demonstrated experience designing and operating services in a major cloud (AWS, GCP, or Azure): deployment, monitoring, and cost awareness, plus solid software-engineering fundamentals (data modeling, API design, testing, debugging, performance) and collaborative Git workflows.
Hands-on experience building with LLMs and AI coding tools via APIs, agent frameworks, MCP, or daily use of coding agents.
A strong communicator who explains technical tradeoffs clearly to engineers and non-engineers, with a creative, detail-oriented, collaborative approach.

Nice To Haves

Experience building agentic systems, LLM tool integrations, or retrieval-augmented applications in production.
Experience designing and maintaining MCP servers, plugin ecosystems, or other extensible tool interfaces for AI agents.
Experience with infrastructure-as-code (Terraform, CDK) and modern CI/CD practices.
Experience with data-intensive systems: pipelines, warehouses, event streams, or workflow orchestration (e.g. Airflow, Prefect, Dagster).
Familiarity with scientific or laboratory software ecosystems (e.g. Benchling, LIMS, instrument integrations), or a strong interest in learning them.
Experience integrating with robotics, hardware, or lab automation systems.
Track record of technical contributions through open-source software, publications, talks, or equivalent demonstrated expertise.
Experience working in a research or early-stage environment where requirements evolve quickly.

Responsibilities

Design, build, and own full-stack systems that power our AI platforms: backend services, data pipelines, frontends, and the cloud infrastructure (AWS and adjacent) behind them, from deployment and observability to compute orchestration for interactive and batch scientific workloads.
Build the 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.
Present your work, mentor teammates, and share software engineering best practices.
Bring high agency and high standards: take ownership, give and receive candid feedback, and push the team to be its best in service of the science.