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 to lead the development of these models: setting modeling direction, designing and running architecture experiments, and owning and implementing innovative architectural improvements. You will also work with our computational chemists to design our internal benchmark and evaluation pipelines.
The RoleIn your first year, you'll take the lead on Gaia-02 model development, building on the work done to train Gaia-01 while moving us towards an innovative, best-in-class architecture.
Lead modeling decisions for Novogaia's core tasks: de novo molecular structure generation, molecular and spectral retrieval, mass spectrum simulation, molecular formula prediction, and compound property/class prediction
Serve as the technical authority for Novogaia’s machine learning program: set modeling and experimentation standards through your own best practices, and lead architecture reviews and experiment design across the team, including:
Making and experimenting with architecture decisions for our spectrum encoders and structure decoders, justifying each choice with experimental evidence
Analyzing model behavior and failure cases, and explaining findings to researchers and non-technical stakeholders alike
Working with engineers to turn training and evaluation code into reproducible, scalable pipelines
Develop new ways to represent mass spectra and molecular structures, such as how peaks, relationships between peaks, and molecular graphs are encoded as model inputs, and design our internal pipelines to benchmark model performance
Collaborate with our computational chemists to build a trustworthy benchmark for structure prediction models, including hard negatives and leakage-safe splits
Follow current research in machine learning and computational mass spectrometry, and propose and prototype relevant approaches from other fields
Translate lessons from experimentation into research priorities, data requirements, and R&D direction
Research or applied experience in machine learning, with direct experience owning foundation model development and generating new modeling ideas
Depth in model architecture: you have designed or modified architectures such as transformers or graph neural networks, and have improved model performance through changes to the architecture, the training objective, or the data representation rather than hyperparameter tuning alone
Exceptional technical communication skills, including the ability to explain evaluation findings clearly to both researchers and non-technical stakeholders
Ability to analyze model behavior and interpret computational results critically
Strong proficiency in Python and experience with reproducible, containerized pipelines
Familiarity with cheminformatics representations (SMILES, InChIKey, molecular fingerprints), or willingness to pick these up quickly
Familiarity with computational mass spectrometry or eagerness to learn
Ability to work in-person in London
Strong scientific judgment and ability to navigate and operate novel research directions
Motivation to build infrastructure other people can confidently rely and build upon
Comfortable being the person who tells the team a result doesn't hold up
Curiosity, low ego, and a willingness to learn the chemistry side quickly
