AI Engineering Lead, LLMs & Lab Data Platform

United States Digital Space LLC; Greater London; Hybrid; GBP 120,000 - 190,000; Full time

Benefits offered by this job

Equity

Learning budget

Annual leave 30 days

Hybrid in-person work

Job summary

Substrate is building autonomous wet-lab infrastructure in London and is hiring an AI engineering lead as the first engineering hire for the intelligence software product. You will write production code, shape the architecture, and influence how the team that comes after you is built.

The role requires five or more years of software engineering experience, direct experience deploying LLMs or agentic systems at scale, strong Python skills, and a track record of designing systems others build on.

Qualifications

• Five or more years of professional software engineering experience.
• Direct experience putting LLMs, foundation models, or agentic systems into production at scale.
• Strong working comfort with Python.
• Track record of designing systems that other engineers build on top of.

Responsibilities

• Define and lock the architecture for AI Scientist and the harness layer underneath it.
• Ship end-to-end features, including literature ingestion, gap identification, and an agentic loop that surfaces candidate experiments.
• Lead engineering culture: code review, evaluation, deployment, observability, tooling decisions.
• Define the data capture infrastructure and feedback paths for AI Assays; wire outputs into R&D capacity allocation.
• Help scope and deliver multiple intelligence products on top of lab data, coordinating across product surfaces.

Skills

Python

Production LLMs

Agentic systems

Foundation models

Data pipelines

Job description

Substrate is building autonomous wet-lab infrastructure in London and is hiring an AI engineering lead as the first engineering hire for the intelligence software product. You will write production code, shape the architecture, and influence how the team that comes after you is built.

The role requires five or more years of software engineering experience, direct experience deploying LLMs or agentic systems at scale, strong Python skills, and a track record of designing systems others build on.