Job Summary

We are seeking an experienced Applied AI Scientist to join our team. The person who fills this role will apply ML & AI principles and practices to large datasets of time-series data and associated metadata to create powerful encoding and decoding models to interpret and understand brain activity, which will drive scientific discovery as well as product implementations to have real-world impact on patients with physical disabilities.

Key Responsibilities

• Modeling brain data and associated metadata to obtain insights into existing and future datasets and implement high-performance inference and translation pipelines
• Designing and creating state-of-the-art transformers and other ML models to decode brain signals into control signals for digital devices
• Keeping up with the latest AI techniques and being able to quickly assess the relevance and utility of these techniques, including with prototypical evaluations with company datasets
• Working collaboratively within a small team to explore and iterate on models, design the company's data-science platforms, and integrate ML models into product applications
• Maintaining versioned, clear, and highly documented code, analysis pipelines, and results for maximum interpretability and reproducibility
• Contribute to documentation for a Quality Management System, as appropriate and relevant to ML implementations that become part of medical products

Required Qualifications

• Bachelor's or Master's degree in Math, Engineering, Data Science, or other relevant quantitative field
• A minimum of 5 years of combined academic and professional ML experience
• Experience designing, developing, and deploying ML models in an industry setting with large-scale, distributed ML platforms
• Experience designing, training, and evaluating transformers
• Experience working with high-dimensional time-series data, state-space models, or foundation models
• Proficiency in Python and PyTorch
• Proficiency in using GPUs and GPU software toolkits to accelerate ML pipelines

Preferred Qualifications

• Experience working on medical or clinical applications
• Experience translating methods described in scientific publications to available datasets