Research Engineer London, UK | Full-time
About the opportunity
We're hiring on behalf of an early-stage company building AI-powered automation tools for automating bio-analysis workflows. Their platform transforms complex, expert-led analytical processes into repeatable, AI-assisted workflows. It's a small team doing serious technical work at the intersection of AI and life sciences.
About the role
The Research Engineer owns the research function. You take a direction from product feedback and turn it into results, scoping, experiment design, implementation, data curation, and interpretation. You'll work closely with biologists, scientific contractors, and the broader team, and act as the technical sparring partner on what to build next.
What you'll do
Turn research directions into projects: scoping, experiment design, implementation, data, and results
Define which metrics matter for agent system performance and which proxies are actually honest
Build the evaluation infrastructure: benchmarks, harnesses, and the tooling around them
Develop the research agent stack: memory, in-context learning, test-time compute, and models
Fine-tune and post-train open models to improve agent performance
Work with biologists, contractors, and annotators to build reproducible training and evaluation data pipelines
Track what frontier labs are shipping and bring back what's relevant
Essential experience
Experience building ML/AI systems in a research-adjacent context, industry, lab, or PhD
Experience building LLM-powered systems: prompts, context engineering, agent architectures
Experience with evaluations and benchmarks, including tasks where "correct" is ambiguous
Familiarity with model training, including the data, optimisation, and evaluation work around it
Strong engineering fundamentals, fluent in Python and comfortable across the AI/ML stack
Rigorous approach to experiments: you think about confounds and can defend your results
Experience with training and evaluation data pipelines, including reproducibility and observability
Nice to have
Background in a life science domain, biology, chemistry, medicine, or bioinformatics
Post-training experience on LLMs
Peer-reviewed publications or other settings where your ideas were stress-tested
Open source contributions to scientific or AI tooling
About the opportunity
We're hiring on behalf of an early-stage company building AI-powered automation tools for automating bio-analysis workflows. Their platform transforms complex, expert-led analytical processes into repeatable, AI-assisted workflows. It's a small team doing serious technical work at the intersection of AI and life sciences.
About the role
The Research Engineer owns the research function. You take a direction from product feedback and turn it into results, scoping, experiment design, implementation, data curation, and interpretation. You'll work closely with biologists, scientific contractors, and the broader team, and act as the technical sparring partner on what to build next.
What you'll do
Turn research directions into projects: scoping, experiment design, implementation, data, and results
Define which metrics matter for agent system performance and which proxies are actually honest
Build the evaluation infrastructure: benchmarks, harnesses, and the tooling around them
Develop the research agent stack: memory, in-context learning, test-time compute, and models
Fine-tune and post-train open models to improve agent performance
Work with biologists, contractors, and annotators to build reproducible training and evaluation data pipelines
Track what frontier labs are shipping and bring back what's relevant
Essential experience
Experience building ML/AI systems in a research-adjacent context, industry, lab, or PhD
Experience building LLM-powered systems: prompts, context engineering, agent architectures
Experience with evaluations and benchmarks, including tasks where "correct" is ambiguous
Familiarity with model training, including the data, optimisation, and evaluation work around it
Strong engineering fundamentals, fluent in Python and comfortable across the AI/ML stack
Rigorous approach to experiments: you think about confounds and can defend your results
Experience with training and evaluation data pipelines, including reproducibility and observability
Nice to have
Background in a life science domain, biology, chemistry, medicine, or bioinformatics
Post-training experience on LLMs
Peer-reviewed publications or other settings where your ideas were stress-tested
Open source contributions to scientific or AI tooling
