We're building a foundation model of the brain and behavior across species, trained on large-scale multimodal neural and behavioral data (including EEG, MEG, and fMRI), and this role is central to designing and training it. You'll work on the core generative model: architecture, training at scale, and representation learning across neural signals and behavior, along with the research questions that come with modeling biological data as sequences. You'll join a small team and work alongside our existing ML engineer, with room to shape the modeling direction as we grow. This is early-stage scope in a much less explored space, so you'll train greenfield models, own parts of the stack, and see your work define the company's core asset.

Model development and training

Design, train, and iterate on large generative (recurrent or transformer-based) models over multimodal neural and behavioral data

Own training at scale: data loading, distributed multi-node training, hyperparameter optimization, and evaluation

Develop representations that capture structure across species and modalities

Train models on animal and human behavioral data as well as direct neural data

Take ownership of distinct components of the modeling stack and deliver them as working, well-documented modules

Research and evaluation

Define and run experiments to test modeling choices, and build the evaluation that tells us whether the model is learning what we need

Draw on the neuroscience and sequence-modeling literature to inform architecture and training, creatively adapting ideas from other modalities (e.g. vision, audio, language) to EEG and other neural signals

Rapidly prototype new ideas and turn research findings into reproducible, production-quality model code

Partner with the data engineering team on data readiness and with the research team on what the model needs to capture

Machine Learning Engineer

Responsibilities

Qualification

Required

Benefits

Company

Netho Labs

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