Materials Informatics Scientist (Evaluation-focused)

Your mission
Define how AI models for materials discovery are evaluated, compared, and trusted

Dunia is building AI for one of the hardest unsolved problems in science: turning materials discovery from an academic, trial-and-error process into a programmable, scalable discipline.

As our models grow more complex and our experimental throughput increases, the limiting factor is no longer generating predictions, but knowing which ones to believe.

As Materials Informatics Scientist (Evaluation-focused), you will own the evaluation and validation of AI models applied to materials discovery. Your role is to ensure that model performance claims are meaningful, comparable, and decision-relevant, and that progress in AI for Materials reflects real improvements in discovery, not artifacts of metrics or datasets.

This role is not about building new models. It is about defining the standards by which models are judged.

Your tasks will include:
Own evaluation as a scientific discipline
Design, implement, and maintain evaluation frameworks for AI models across materials discovery tasks
Define metrics and protocols that reflect generalization, robustness, uncertainty, and experimental relevance
Identify failure modes, dataset leakage, and misleading performance signals
Interrogate and compare models
Systematically benchmark different model classes, training regimes, and representations
Evaluate tradeoffs between accuracy, uncertainty, data efficiency, and usability
Provide clear, defensible recommendations on which models to trust, deploy, or retire
Connect AI performance to real outcomes
Link model behavior to experimental results and program-level objectives
Distinguish improvements that change decisions from those that only improve abstract scores
Help research and programs teams understand what current models can and cannot reliably do
Build robust analytical tooling
Develop and maintain professional-grade scripts and analysis pipelines for evaluation and benchmarking
Visualize complex, high-dimensional results in ways that surface real insight
Ensure disciplined, reproducible handling of data, code, and results
Communicate truth clearly
Present findings clearly to AI researchers, materials scientists, and leadership
Produce concise summaries that align the organization around a shared view of evidence and uncertainty
Act as an independent scientific reference point when claims require validation
Your profile
PhD (strongly preferred) or Master’sdegree in materials science, chemistry, physics, machine learning, or a related field
Significant experience (typically 5–8 years) working with scientific or ML systems under real-world uncertainty
Demonstrated experience evaluating, benchmarking, orvalidatingmodels rather than only building them
Strong programming skills in Python and scientific data tooling
Deep appreciation for rigor, reproducibility, and careful interpretation of results
English fluency, additional languages preferred