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 performanceWork 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
* 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
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