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 Machine Learning Scientist to develop the modeling approaches behind our non-invasive neural interface. You will work on learning from neural and multimodal time-series data to help infer human intent and enable new forms of interaction with AI systems. This is a hands-on research role for someone who enjoys developing new ML methods, running rigorous experiments, and turning promising ideas into working prototypes.
The role spans representation learning, self-supervised learning, neural decoding, model evaluation, personalization, and real-time adaptation, while working closely with ML engineers, neuroscientists, software engineers, and product teams.
Develop new machine learning approaches for neural and multimodal time-series data
Design experiments, benchmarks, and ablations to evaluate model performance, robustness, and generalization
Translate research ideas into working prototypes that can be tested on real data and in user studies
Collaborate with BCI scientists and ML engineers to turn neuroscience-informed modeling ideas into reliable training and inference workflows.
5+ years of experience in machine learning research, applied ML research, or a related field
PhD, MS, or equivalent research experience in machine learning, computer science, computational neuroscience, applied mathematics, physics, electrical engineering, or a related field
Strong experience with deep learning for time-series, sequential, multimodal, or sensor data
Strong experience with PyTorch, JAX, TensorFlow, or similar frameworks
Strong understanding of representation learning, self-supervised learning, transformers, generative models, or foundation models
Experience designing rigorous ML experiments, benchmarks, ablations, and evaluation protocols
In case of any doubts or questions, please contact - [email protected]
