Principal Software Engineer (f/m/d) - Lab Automation

Description

The company uses AI, data and computational methods to speed up drug discovery and development across its research organisations.

A new Computational Sciences Center of Excellence brings these efforts together to give scientists in both organisations more data-driven tools for developing medicines for patients.

Within it, the Data and Digital Catalyst group is modernising computational and data systems and bringing digital technology into research and early development.

You would join a team of software engineers building the software and hardware layers of automated lab systems, so scientists get reliable, monitored tools.

Responsibilities

• Write software that drives lab instruments such as liquid handlers, plate readers and robot arms, and build their drivers and APIs over serial, USB and TCP/IP.
• Lead work on drivers, APIs and middleware that link custom and commercial robots with LIMS and ELNs.
• Design agentic systems and digital lab twins that run scientific experiments, reasoning about sample identity, instrument status and resource availability.
• Set the technical roadmap in line with the CoE's goals, working with stakeholders on a long-term, standard and scalable framework.
• Act as the senior technical adviser to engineers across lab automation on system control, data capture and workflow connectivity.
• Apply reinforcement learning, in-context learning or reasoning to improve dynamic scheduling and the workflow engines that run the lab.

Requirements

• A degree in computer science or a related technical field, or equivalent hands-on experience, plus substantial professional experience, ideally 8+ years.
• Practical experience in a biotech or pharmaceutical research setting.
• Expert-level Python and/or TypeScript skills and deep knowledge of cloud-native design (ideally AWS or GCP), RESTful APIs, microservices, serverless and distributed systems.
• Experience with at least two protocols or technologies such as NATS, Kafka, MQTT or GraphQL.
• A clear view on how a team can use AI coding workflows, with AI tools as core software platforms (for example loop and graph engineering), to ship better code faster.
• You have owned or shaped a technical roadmap, decided what to build, defer or retire, and make pragmatic trade-offs.
• You communicate well, build trust with scientists and can define high-level architecture for complex work.
• Preferred but not required: experience training, evaluating or adopting AI/ML models, including reinforcement or imitation learning for robotics, and building AI-enabled systems with evals, guardrails and reliability measures.

Conditions

The company is an equal opportunity employer.