Research Engineer - Brain Computer Interface Models

Benefits

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Mid From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Application

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Deadline

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About this role

Research Engineer - Brain Computer Interface Models San Francisco, California, United States 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

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