Brain-computer Interface Scientist

Noïa Labs is an early-stage neurotechnology company building the next generation of human-AI interfaces.

We are working on one of the most ambitious problems in human-AI interaction: creating a more natural way for people to control guide and collaborate with AI systems by relying directly on brain activity. Our approach combines optimized non-invasive neural sensors with large-scale AI models trained across many users to decode human intent from brain signals without surgery.

Noïa Labs was founded by the team behind NextMind (acquired by Snap) and is backed by tier-1 investors. We are at the beginning of the journey and are building a team of outstanding engineers and scientists where each person can have a major impact on the product and technology.

We are seeking a talented BCI Scientist to lead the neural decoding efforts behind our non-invasive neural interface. You will work on identifying the brain-state markers that are stable generalizable and decodable at scale and turning neuroscience insight into practical ML-ready approaches.

This is a hands-on scientific role at the intersection of neuroscience and machine learning: you will design experiments analyze neural and behavioral data and own the decoding validation pipeline. The role spans experimental design signal analysis decoding metrics user studies and collaboration with ML and product teams.

• Develop machine learning models for decoding neural and physiological time-series

• Design experiments benchmarks and ablations to evaluate performance robustness latency and generalisation.

• Study how models adapt across users sessions tasks sensors and contexts

• Work with data-collection teams to define labels tasks and annotation protocols for training-ready neural data

• Collaborate with ML engineers to turn promising methods into reliable training and inference workflows

• 5 years of experience in ML computational neuroscience or a related field.

• Experience building ML or deep-learning models for time-series data

• Experience with neural or physiological signals such as EEG MEG ECoG EMG ECG eye tracking PPG or related modalities

• Strong experience with Python and modern ML frameworks such as PyTorch JAX or TensorFlow

• Strong understanding of signal processing statistics model evaluation and experimental design

• Experience with personalization domain adaptation transfer learning few-shot learning or online adaptation

• Experience with real-time inference edge deployment or low-latency ML systems

• Experience with MNE Braindecode scikit-learn NumPy/SciPy or similar tools

• Experience with transformers self-supervised learning representation learning foundation models or multimodal models for neural or sensor data

• Experience translating research into products or deployed systems

• Experience working in a fast-moving startup or research-to-product environment

In case of any doubts or questions please contact -

Required Experience:

IC