Machine Learning Engineer

We're building a foundation model of the brain and behavior across species, trained on large-scale multimodal neural and behavioral data, 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, so you'll train greenfield models, own parts of the stack, and see your work define the company's core asset.

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 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

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

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

Contribute to the shared modeling roadmap alongside our existing ML engineer

You’ve trained large deep learning models end to end, in production or research settings

Hands-on experience training transformer or other large sequence models, including distributed training and scaling

Solid software fundamentals: Python and PyTorch (or JAX), and the discipline to write reproducible model code

Comfort working with large, messy, multimodal or time-series data

Pragmatism for an early-stage environment where you own work from end to end

Enthusiasm for the science of modeling biological data and the intersection of the brain and AI

Familiarity with representation learning and self-supervised or generative modeling

Background or strong interest in neuroscience, biosignals, or computational cognitive science

Experience with hyperparameter optimization, training infrastructure, or evaluation frameworks

Publications or open-source contributions in relevant areas