Machine Learning Engineer

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 (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.

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 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

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

• Communicate your process, results, and trade-offs clearly to technical and scientific colleagues

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

• Proficiency in Python and PyTorch, with clean, concise coding practices and the discipline to write reproducible model code

• Strong implementation and prototyping skills, and comfort working across both research and engineering at scale

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

• Ability to learn new domains quickly, orient yourself in the academic literature, and implement new ideas

• An organized, methodical approach to research, with excellent communication and collaboration skills

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

Valued

• Enthusiasm for the science of modeling biological data, the intersection of the brain and AI, and building foundation models for neural data such as EEG, MEG, and fMRI

• Experience with representation learning and self-supervised or generative modeling, such as VAEs, GANs, diffusion models, and contrastive learning

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

• Familiarity with signal processing (especially for EEG) and time-series analysis

• Experience training on large-scale, multi-node GPU clusters, or with hyperparameter optimization, training infrastructure, or evaluation frameworks

• Experience contributing to large existing codebases and ramping up quickly

• Published machine learning research in well-respected venues, or open-source contributions in relevant areas