Junior AI/ML Engineer — Neurobehavioural AI (Remote/Hybrid)

Netholabs Ltd.; San Francisco (CA); Hybrid; USD 110,000 - 150,000; Full time

Job summary

Netholabs is building AI grounded in biological intelligence. We record petascale neurobehavioural data and use it to train neural foundation models for robotics and embodied AI.

We are seeking an AI/ML engineer to support the research and engineering team across model training, data pipelines, and applied ML work. This is a broad, hands-on role with opportunities to work on active projects and grow into areas that fit you best.

Qualifications

• Strong Python skills and Linux/CLI experience.
• Solid foundation in ML fundamentals from 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 with ambiguity in fast-moving research.
• Good communication; able to document work clearly as you go.

Responsibilities

• Support training, fine-tuning, and evaluation of neural foundation models.
• Run experiments, track results, and iterate on model architectures.
• Work with biomechanical data, machine vision data, and robotics control problems.
• Help benchmark model performance and write up findings.
• Build and maintain data pipelines for petascale neurobehavioural datasets.
• Clean, preprocess, and structure multi-modal data for training.
• Help keep experiment tracking, datasets, and compute usage organized.
• Prototype small tools, scripts, and demos to test model capabilities.
• Contribute to internal documentation as work progresses.

Skills

Python

ML fundamentals

PyTorch

Time-series ML

Communication

Job description

Netholabs is building AI grounded in biological intelligence. We record petascale neurobehavioural data and use it to train neural foundation models for robotics and embodied AI.

We are seeking an AI/ML engineer to support the research and engineering team across model training, data pipelines, and applied ML work. This is a broad, hands-on role with opportunities to work on active projects and grow into areas that fit you best.