# About the role
Science Machine is building agentic AI software for automating bioanalysis workflows. Our platform helps scientists and bioanalysis teams turn complex analytical processes into reliable, repeatable, AI-assisted workflows across data ingestion, analysis, reporting, review, and compliance.
The Research Engineer executes our research projects. The work spans agent systems, how we quantify their performance, and fine-tuning open models. Research direction comes from product feedback; you turn it into shipped results.
You will work close to the science: formulating problems, designing experiments, shipping implementations, curating data, and interpreting results. You will work with biologists, scientific contractors, and the rest of the team, and act as a 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 how we measure system performance: which metrics matter, which proxies are honest, which signals to trust.
– Build the evaluation infrastructure to put those metrics into practice: benchmarks, harnesses, and the tooling around them.
– Develop the research agent stack: memory and in-context learning, test time compute, and models.
– Fine-tune and post-train open models to improve agent performance.
– Work with biologists, scientific 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 working with evaluations and benchmarks, including in tasks where “correct” is ambiguous.
– Familiarity with model training generally, including the data, optimisation, and evaluation work around it.
– Strong engineering fundamentals. Fluent in Python and comfortable across the AI/ML stack.
– Experience running experiments rigorously, in academia or industry. You think about confounds. You can defend your results.
– Experience with training and evaluation data pipelines, including reproducibility and observability.
## Nice to have
– Experience in a life-science domain (biology, chemistry, medicine, 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.
## Essential qualities
– High ownership: you notice what needs doing and carry it through.
– Skeptical of your own results: you assume a good-looking number is wrong until you understand why it isn't.
– Strong opinions, weakly held: you push back, defend a position, and change your mind when evidence moves.
– Hands-on with the unglamorous parts: data cleaning, contractor coordination, eval annotation, whatever the project needs.
– Reliable under ambiguity: you make progress when problems aren't yet well-defined.
– Curious about science: you want to learn the bioanalysis domain.
– Clear communicator: you can explain technical decisions and results to engineers, scientists, and founders.
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