Research Engineer - Brain Computer Interface Models

Bison; San Francisco (CA); On-site; USD 120,000 - 210,000; Full time

Benefits offered by this job

Comprehensive health, dental, vision

401(k) plan

Relocation and immigration support

In-office snacks and meals

Unlimited PTO

In-person team in San Francisco

Job summary

Zyphra, a San Francisco-based AI company, is seeking a Research Engineer - Brain Computer Interface Models to join our BCI program. You will contribute across the full model lifecycle—from data collection and preprocessing to designing novel architectures and training methodologies for EEG and related modalities.

You'll collaborate with researchers and engineers to push the boundaries of brain-computer interfaces while deploying models in real-world applications.

Qualifications

• Strong ownership and ability to deliver a working module.
• Excellent communication and clean coding practices.
• Experience with large-scale multi-node GPU training and EEG-related prototyping.

Responsibilities

• Train large-scale EEG models and evaluate performance.
• Design ablations and innovations in architectures and training methods.
• Collect and preprocess EEG and other BCI data.
• Optimize the training stack for performance.
• Integrate models into real-world BCI applications.

Skills

Ownership

Communication

Prototyping

Cross-functional collaboration

Tools

PyTorch

Python

GPU clusters

Git

Job description

Zyphra is an artificial intelligence company based in San Francisco, California.

The Role:

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

What We're Looking For / 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

Qualifications / Additional Skills:

• 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

Why Work at Zyphra:

• 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

Benefits and Perks:

• 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