Zyphra
Research Engineer - Brain Computer Interface Models
3mo ago
USApytorcheegmodel trainingdata preprocessingperformance optimization
Research Engineer position focusing on building next-generation brain-computer interface models using EEG data at an AI company.
Responsibilities
- 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.
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
- 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
Conditions
- 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
Other
- 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