AI/ML Internship

Type: Internship, 6 months, flexible start date
Location: SF, USA, remote — On-site/hybrid possible

Please not the successful candidate must be eligible to work in the US, we are not able to sponsor visas at this time

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.