BCI Clinical Field Engineer - Neuralink

Job Description and Responsibilities:

As a BCI Clinical Field Engineer, you’ll collaborate with software engineers, neuroengineers, animal trainers, mechanical engineers, and clinical team members to develop the next-generation human Brain-Computer Interface (BCI). 

The overarching responsibility of the Field Engineer is to be the interface between the Neuralink users and the other engineers of both the BCI application team and the broader company. As the bridge between the users and other engineers, there are two general roles field engineers serve:

1. Understand and communicate user needs: 

• Interface with software and ML engineers to translate user needs into prioritized engineering goals
• Enhance user experience through supporting users with adopting the BCI, optimizing physical setup, and troubleshooting bugs
• Conduct user research through interviewing patients with quadriplegia, informing long-term product vision

      2. Conduct and organize research experiments:

• Run daily sessions with clinical trial participants to evaluate neural decoding algorithms and user interface features (may require travel)
• Design experiment methodology (e.g. data collection practices, A/B tests, and qualitative frameworks)
• Analyze data and drive insights that guide R&D decisions (e.g. behavioral, BCI performance, and electrophysiological data)
• Run non-human primate (NHP) BCI research sessions

Required Qualifications: 

• Bachelor's Degree in a science or engineering field (e.g., neuroscience, biology, engineering, physics, software), or equivalent experience
• Excellent interpersonal and communication skills
• Experience working with users for a product or service
• Experience in data collection and analysis (in Python)

Preferred Qualifications:

• Experience working with participants in clinical studies
• Experience prototyping and designing a product or service for users
• Proficiency in collecting physiological or other time-series data
• Experience collecting and analyzing user feedback