Applied AI Engineer, Beneficial Deployments (Lab Automation)

London · GBP 225,000-255,000 / year · Hybrid · Posted Oct 13, 2026

At a glance

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

The Applied AI Engineer will join the Beneficial Deployments, Life Sciences team to help Claude drive scientific hardware and software.
This role focuses on deploying the Model Hardware Standard (MHS) to labs to automate experiments and close the loop on scientific discovery.
The engineer will scale field learnings by contributing to the standard, building assets, and driving internal product and research direction.

Work with labs to automate their experiments end-to-end with Claude, from advising to prototyping and writing production code.
Drive product, engineering, and research direction based on field experience.
Build evals to measure model limitations in the physical world regarding capabilities and safety.
Develop new features on the Model Hardware Standard (MHS).
Scale impact to the long tail of labs through reusable cookbooks, documentation, and other assets.
Serve as an EMEA ambassador for Claude in the lab to inspire and build trust with scientific audiences.
Travel significantly to engage with labs and partners.

Hands-on experience connecting software to lab robots and instruments such as liquid handlers, plate readers, scheduling software, and LIMS.
Strong engineering skills to take an LLM-powered tool from prototype to production code.
Ability to write evals to measure performance.
Scientist's instinct for experimental workflows, identifying automation opportunities, and assessing AI safety in the physical world.
Experience working directly with external customers or partners in a technical role.
Clear written and verbal communication skills.
Bachelor’s degree or an equivalent combination of education, training, and/or experience.

Location-based hybrid policy: staff are expected to be in the office at least 25% of the time.
Some roles may require more time in the office.
This role requires significant travel.