Machine Learning Scientist
Role details
Job location
Tech stack
Job description
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.
In this role, you will
- 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.
Requirements
Do you have a Doctoral degree?, * 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