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 RoleWe'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.
ResponsibilitiesModel 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
RequirementsCore (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
