Machine Learning Researcher

Alljoined is creating a future where humans are fully understood and augmented by technology. Our work solves the communication bottleneck between humans and computers by decoding thoughts from the brain, entirely non-invasively. We apply deep learning research to large scale neural datasets to decode internal thought directly. By advancing the frontier of neural decoding, we aim to unlock meaningful breakthroughs in human wellness and capability.

We 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.

• 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.

• 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.

We 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.