About the RoleWe are looking for a talented Machine Learning Researcher to join our core R&D team. You will design and implement advanced machine learning models for EEG-based neural decoding, contribute to high-impact research, and help build the foundational infrastructure behind our brain-decoding systems.
You will work closely with leading experts in neural decoding and AI to push the boundaries of what is possible in brain-computer interfaces. This role sits at the intersection of ambitious research and rigorous engineering: you will explore novel modeling approaches while translating promising ideas into reliable, production-quality systems.
What You'll Work On- Develop, train, and refine state-of-the-art deep learning models for neural decoding, drawing on recent advances in architectures such as transformers and diffusion models.
- Explore novel methods for modeling high-frequency, time-series EEG data alongside several adjacent data modalities.
- Translate research insights into production-grade code that integrates seamlessly with our in-house BCI stack.
- Collaborate with neuroscientists and machine learning engineers to build scalable, end-to-end neural-decoding systems.
- Publish findings at leading machine learning and AI conferences, including NeurIPS, ICML, ICLR, and CVPR.
- Contribute to open-source communities where appropriate.
You May Be a Good Fit If You Have- A bachelor's degree in computer science or a related field-such as artificial intelligence, computational neuroscience, mathematics, or biomedical engineering-and five to seven years of experience in machine learning research or applied machine learning engineering; or
- A graduate degree (M.S. or Ph.D.) in computer science or a related field-such as artificial intelligence, computational neuroscience, or biomedical engineering-and at least three years of experience in machine learning research or applied machine learning engineering.
- A track record of high-quality research, demonstrated through publications at leading machine learning conferences or in respected journals, including NeurIPS, ICML, ICLR, or CVPR.
- Strong proficiency in Python and PyTorch, along with familiarity with modern machine learning tooling and distributed training.
- Experience contributing to a production-quality codebase with modern code-review standards.
Candidates with a Ph.D. and/or experience working in a high-profile machine learning research lab are strongly preferred.
Areas of Relevant ExpertiseWe are particularly interested in candidates with experience in one or more of the following areas:
- Multimodal representation learning: CLIP-style contrastive objectives and masked autoencoding.
- Generative modeling: Diffusion models, transformer decoders, and latent GANs.
- Temporal sequence modeling: State-space models, STFT-aware transformers, and RWKV.
Benefits- Options for housing support
- Visa sponsorship
- Health insurance
