VP of NeuroAI
Job in
San Francisco, San Francisco County, California, 94199, USA
Listed on 2026-06-18
Listing for:
Gestala
Full Time
position Listed on 2026-06-18
Job specializations:
-
IT/Tech
Systems Engineer, Machine Learning/ ML Engineer, AI Engineer (Applied/Software) -
Engineering
Systems Engineer, AI Engineer (Applied/Software)
Job Description & How to Apply Below
- NeuroAI Modeling Architecture:
Design a unified modeling framework from neural signals to latent representations and behavioral outputs;
Define abstraction levels (cell / voxel / population) and temporal dynamics;
Lead both decoding (read) and encoding (write) model development. - Multimodal Brain-Behavior Modeling:
Integrate neural data (ECoG, LFP, ultrasound, etc.) with behavior, video, and language;
Build cross-modal alignment models for brain-native representation learning. - Closed-loop System Development:
Develop real-time brain decoding and stimulation feedback systems;
Collaborate with experimental teams to design data collection and validation protocols. - System & Infrastructure:
Build scalable data pipelines and training infrastructure (large-scale, multi-modal);
Ensure low-latency inference and system reliability. - Team Leadership:
Build and lead a cross-disciplinary team across AI, neuroscience, and systems engineering;
Define technical roadmap from early prototypes to clinical-grade systems. - Strong background in machine learning (deep learning, sequence modeling, or multimodal models). Experience with neural data or brain-computer interfaces (ECoG, spike, LFP, etc.)
- Proven experience in building scalable AI systems or platforms
- Demonstrated leadership in technical teams
- Neural decoding / representation learning experience, multimodal foundation models (video, language, etc.), reinforcement learning / closed-loop control systems, and experience in real-time systems or edge deployment preferred
- Ability to translate scientific questions into engineering systems, comfortable working in high-uncertainty, 0→1 environments, and strong first-principles thinking
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