# 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….