AI/ML Internship

Type: Internship, 6 months, flexible start date
Location: SF, USA, remote — On-site/hybrid 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 an AI/ML Intern to support our research and engineering team across model training, data pipelines, and applied ML work. You'll get hands-on exposure to how a neural foundation model is actually built — from raw neurobehavioural data through to training runs and downstream applications in robotics and embodied AI. This is a broad, hands-on role: you'll work closely with our research engineers, take on real pieces of active projects, and grow into the areas that fit you best.

Responsibilities

Model Development

• Support training, fine-tuning, and evaluation of neural foundation models

• Run experiments, track results, and help iterate on model architectures

• Help benchmark model performance and write up findings

Data & Infrastructure

• Build and maintain data pipelines for petascale neurobehavioural datasets

• Clean, preprocess, and structure multi-modal data (video, sensor, physiological) for training

• Help keep experiment tracking, datasets, and compute usage organized

Applied ML & Robotics

• Support applications of trained models to robotics and embodied-agent tasks

• Prototype small tools, scripts, and demos to test model capabilities

• Contribute to internal documentation as work progresses

Requirements

Core (essential)

• Strong Python skills and comfort working in a Linux/command-line environment

• Solid foundation in ML fundamentals (e.g., through coursework, projects, or research)

• Experience with at least one deep learning framework (PyTorch preferred)

• Experience training ML models on time-series datasets

• Curious, self-directed, and comfortable working with ambiguity in a fast-moving research environment

• Good communication; able to document work clearly as you go

Valued (or willing to learn)

• Exposure to large-scale model training or distributed compute

• Experience with data pipelines, structured storage, or large dataset handling

• Familiarity with robotics, sensorimotor learning, or embodied AI

• Background in neuroscience, behavioural science, or related fields

No prior neuroscience or robotics experience required; we will cross-train the right person.