Research Engineer - Brain Computer Interface Models

As a Research Engineer - Brain Computer Interface Models, you will be a core contributor to Zyphra's BCI work, building the next generation of open-source EEG and brain–computer interface models. You will be involved across the full model lifecycle, from data collection and preprocessing to designing novel architectures and training methodologies. You'll work across: Large-scale EEG model training runs and evaluation; Architecture and training methodology ablations and innovations; Dataset collection and preprocessing for EEG and other BCI modalities; Performance optimization of the training stack; Integration of models into real-world BCI applications.

Responsibilities

Core contributor to Zyphra's BCI work, building the next generation of open-source EEG and brain–computer interface models; Involved across the full model lifecycle, from data collection and preprocessing to designing novel architectures and training methodologies; Large-scale EEG model training runs and evaluation; Architecture and training methodology ablations and innovations; Dataset collection and preprocessing for EEG and other BCI modalities; Performance optimization of the training stack; Integration of models into real-world BCI applications

Requirements

Organized, methodical researcher who can take ownership of a project component, communicate their process clearly, and deliver a working module; Excellent communication and collaboration skills, with clean and concise coding practices; Strong implementation and prototyping skills, with comfort working across both research and engineering at scale; Ability to learn new domains quickly, orient oneself in the academic literature, and implement new ideas, creatively borrowing concepts from other modalities and applying them to EEG domain; Interest in neural data such as EEG, MEG, fMRI as well as building foundation models in a much less explored space; Proficiency with PyTorch and Python

Nice to have

Experience with VAEs, GANs, diffusion models, and contrastive learning; Familiarity with signal processing (especially for EEG) and time-series analysis; Experience training on large-scale (multi-node) GPU clusters; Experience contributing to large existing codebases and ramping up quickly; Previously published machine learning research in well-respected venues

Benefits

Comprehensive medical, dental, vision, and FSA plans; Competitive compensation and 401(k) plan; Relocation and immigration support on a case-by-case basis; In-office snacks and meals provided; Unlimited PTO and company holidays; In-person team in San Francisco with a collaborative, high-energy environment