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San Francisco, California, United States
Onsite
We’re a team of engineers, neuroscientists, and designers solving the most difficult and meaningful challenge: understanding the human brain. Our translational brain computer interface and pioneering models decode emotion, putting experience and wellbeing at the center of every interaction.
Our wearable BCI achieves fMRI-comparable resolution untethered to the lab. It’s this advancement that enables us to build foundation models of emotion.
We’re looking for people to help us build and scale. If you want to work on deep technology with real impact, and help define the future of brain-computer interfaces and AI, join us.
We’re backed by the founders and execs of the leading companies in AI, neurotech, consumer hardware and pharmaceuticals - including Google, Hugging Face, Apple, Stability, Microsoft and Dropbox. We’re venture funded.
We’re building a generational founding team which is truly full-stack - from neural sensors to complex models. If you want to work on deep technological problems and help pioneer the future of NeuroAI, this is the place for you. Projects have opportunities for a high degree of autonomy and demand intense, fast-paced learning.
Critically evaluate and implement the best machine learning approaches for our unique design problems in neural data
Work with real-time, multi-dimensional, multimodal datasets
Collaborate closely with neuroscience, hardware, and software teams to co-design end-to-end systems
Explore new model architectures and perform detailed experimentation and analysis
Learn neuroimaging and neuroscience context (we will support you in getting up to speed)
An BS or higher in Computer Science, Electrical Engineering, Applied Mathematics, or a related STEM field (exceptional self-taught researchers also considered)
3+ years of applied ML research or development experience, or equivalent depth through publications, projects, or startup work
Strong Python programming skills with experience in PyTorch, TensorFlow, or JAX
Built and iterated quickly on ML models and pipelines
Experience with data preprocessing, labeling, and exploratory analysis
Agility working with multimodal data (e.g., imaging + time series, text + audio)
Proven ability to thrive in small, fast-moving teams
Publications in top ML or domain-specific journals/conferences
Experience with biomedical, neuroimaging, or other high-dimensional sensor data
A background in signal processing for time-series or imaging data
Experience with distributed or large-scale training (e.g., mixed precision, very large datasets)
Knowledge of semi-supervised or self-supervised approaches
Excitement to learn neuroimaging and neuroscience context
