Type: Internship, 6 months, flexible start date
Location: SF or USA — On-site/hybrid/remote possible
The Role
Netholabs is building AI grounded in biological intelligence. We record petascale, high-resolution neurobehavioural data from living systems and use it to train neural foundation models — a new substrate for the next generation of AI, robotics, and personalized intelligence.
We're looking for a Neuro for AI Intern to help connect the neuroscience side of our work to the models we're building. You'll spend time close to the data — supporting recording sessions, annotating neural and behavioural datasets, and digging into the literature — and use that grounding to help translate biological principles into features, experiments, and model design choices. This is a hybrid role for someone who wants a foot in both neuroscience and ML.
Responsibilities
Data Collection & Annotation
Support neurobehavioural recording sessions and data capture
Annotate and label neural/behavioural datasets for model training
Help maintain data quality, structure, and documentation standards
Research & Literature Synthesis
Review neuroscience and behavioural science literature relevant to active projects
Summarize findings and surface ideas that could inform model design
Help track open questions and relevant research across the field
Data Analysis
Analyze neural and behavioural data feeding into foundation model training
Build small scripts/notebooks to explore and visualize datasets
Support quality checks and validation of processed data
Neuroscience ↔ ML Bridging
Help translate biological principles into model features or architectures
Support design and running of ML experiments informed by neuroscience insights
Contribute to internal write-ups connecting findings back to the modeling team
Requirements
Core (essential)
Background in neuroscience, behavioural science, cognitive science, or a related field (coursework, research, or project experience)
Proficient in Python, including experience building and training models in PyTorch — comfortable writing and debugging code, not just running analyses in notebooks
Able to read and synthesize scientific literature clearly and critically
Curious and comfortable working across disciplines in an ambiguous, fast-moving research environment
Good communication; able to document work clearly as you go
Valued (or willing to learn)
Exposure to neural/behavioural data (e.g., electrophysiology, video-based behaviour tracking, motion capture)
Experience with time-series data such as EEG
Experience with data annotation or structured dataset curation
Interest in robotics, embodied AI, or computational neuroscience