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.

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

Our research methodology is grounded in methodical, step-by-step approaches to ambitious goals. Both deep research and engineering excellence are equally valued

We strongly value new and crazy ideas and are very willing to bet big on new ideas

We move as quickly as we can; we aim to minimize the bar to impact as low as possible

We all enjoy what we do and love discussing AI

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

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