About the job
About Alljoined
At Alljoined, we are pioneering the future of communication between humans and technology by developing non-invasive methods to decode brain activity. By leveraging cutting-edge deep learning techniques on extensive EEG datasets collected through cost-effective hardware, we aim to interpret images, text, and video, with a long-term vision of understanding internal thoughts. Our capabilities are industry-leading, and we are fully vertically integrated. Our mission is to create a universal consumer interface that revolutionizes daily interactions both at home and in the workplace.
We are on the lookout for exceptional researchers to expand our elite team, dedicated to creating the next transformative interface that enhances individual lives and contributes positively to society.
About the Role
We invite you to apply for the position of Machine Learning Researcher within our core R&D team. In this role, you will be responsible for conceptualizing and executing advanced machine learning models for EEG-based neural decoding, disseminating impactful research, and establishing the foundational infrastructure for our brain decoding systems. You'll collaborate with top-tier experts in neural decoding and AI, driving innovation in brain-computer interfaces.
Key Responsibilities
Research & Model Development:
Craft, train, and enhance state-of-the-art deep learning models for neural decoding, utilizing the latest advancements in machine learning architectures such as transformers and diffusion models.
Investigate innovative methodologies for modeling high-frequency time-series EEG datasets alongside various other data modalities.
Convert research findings into production-ready code that seamlessly integrates with our proprietary brain-computer interface stack.
Collaboration & Publication:
Work in tandem with a multidisciplinary team of neuroscientists and ML engineers to develop scalable, end-to-end neural decoding solutions.
Publish research outcomes in leading ML and AI conferences such as NeurIPS, ICML, ICLR, and CVPR, and actively engage in open-source communities as appropriate.

