Overview

Novogaia is an applied AI drug discovery company. We build machine learning systems that decode the chemistry of natural organisms, starting with fungi, to find the next generation of medicines.

We are a small team of AI engineers, computational biologists and chemists building foundation models for molecular structure prediction from mass spectrometry data. We are seeking a machine learning research engineer who excels at designing rigorous tests for molecular AI systems, and who can turn "does this model actually work" into a concrete, defensible answer. You will own the design of our internal benchmarks and evaluation pipelines; build the adversarial checks that catch shortcut learning and leakage. You will work closely with our modeling team to translate evaluation results into research priorities.

The Role

• Develop a deep understanding of Novogaia's models, data, and evaluation needs
• Design benchmark tasks that reflect real discovery problems: de novo molecular structure generation, molecular and spectral retrieval, mass spectrum simulation, molecular formula prediction, and compound property/class prediction
• Build the datasets and controls that make a benchmark trustworthy: hard negatives, leakage-safe splits, and null baselines that catch a model exploiting shortcuts instead of genuine signal
• Communicate evaluation results as clear findings for the modeling team, and as documentation and data cards that others can trust and reproduce
• Work across the team to scope and lead evaluation work, including:
  • Defining what "good" looks like for a given model or task, and choosing the right test for it
  • Analyzing model behavior and interpreting results for researchers and non-technical stakeholders alike
  • Working with engineers to turn one-off analyses into repeatable, reproducible evaluation pipelines
  • Translate lessons from evaluation work into research priorities, data requirements, and R&D direction