As a Neuroscience Community Engineer, you will act as the liaison between the systems neuroscience community and the technical staff team. You will maintain high-quality teaching and documentation materials and promote our resources at external events such as conferences, courses and hackathons. You will assess our impact on the field by developing analytics and tracking adoption metrics. Beyond traditional outreach, you will also maintain AI-driven workflows to streamline how scientists interact with and utilize neuroscientific data with modern LLMs.
Key Responsibilities
Technical Documentation: Author, deploy, and manage public-facing documentation for the IBL portfolios of datasets and tools.
Teaching Assistant: Author and deliver systems neuroscience tutorials and courses at remote and in-person events.
Impact Analytics: Quantify the reach of our resources by tracking citations, download metrics, and data re-use across the field.
AI Integration: Maintain AI agentic workflows for the community to access and interact with our data: prompts, agent files, skill files, MCP.
Data Publishing and Dissemination: Lead the preparation and release of large datasets, optimizing formatting, metadata, and accessibility to maximize community uptake.
Skills and Attributes for Success
Neuroscience Expertise: Knowledge and understanding of the field of systems neuroscience current landscape.
Scientific Programming: autonomy in using our scientific libraries and reproducing existing results for building teaching materials.
Communication: Exceptional public speaking skills
Mobility: Ability to travel overseas approximately one week per month for conferences and workshops.
AI Adopter: Enthusiastic about AI for science technologies
Minimum Qualifications
Education: Master’s degree in Computer Science, Neuroscience, or a related field.
Expertise: 2+ years of experience in systems neuroscience.
Preferred Qualifications
Experience as a TA
Experience releasing and maintaining open-source datasets