Senior Machine Learning Engineer (Foundation Models)

Type: Full-time

Location: San Francisco, United States. In person.

At this time we are only able to hire candidates who are already based in the US and able to work in-person in San Francisco. We do not support relocation at this time.

PLEASE DO NOT USE AI IN YOUR APPLICATION.

The Role

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.

Responsibilities

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 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

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
• Turn research findings into reproducible, production-quality model code

Collaboration

• 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

Requirements

Core (essential)

• 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

Valued

• 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