Junior AI/ML Engineer

Netholabs•San Francisco, CA

20h

About The Position

Netholabs is building AI grounded in biological intelligence by recording high-resolution neurobehavioural data from living systems and using it to train neural foundation models. The Junior AI/ML Engineer will support the research and engineering team across model training, data pipelines, and applied machine learning. The role offers hands-on exposure to building neural foundation models, from raw neurobehavioural data through training runs and downstream applications in robotics and embodied AI. The successful candidate will work closely with research engineers on active projects and grow into areas that best fit their skills and interests. The role is full-time and entry level, based in San Francisco, USA, with remote work and possible onsite or hybrid arrangements. Candidates must be eligible to work in the US or UK; visa sponsorship is not available. The stated start date is ASAP.

Requirements

Strong Python skills and comfort working in a Linux/command-line environment.
A solid foundation in machine learning fundamentals, gained through coursework, projects, or research.
Experience with at least one deep learning framework; PyTorch is preferred.
Experience training machine learning models on time-series datasets.
Curiosity, self-direction, and comfort working with ambiguity in a fast-moving research environment.
Good communication skills and the ability to document work clearly.
Eligibility to work in the US or UK; visa sponsorship is not available.

Nice To Haves

Experience with machine vision or robotics problems.
Exposure to large-scale model training or distributed compute.
Experience with data pipelines, structured storage, or handling large datasets.
Familiarity with robotics, sensorimotor learning, or embodied AI.
A background in neuroscience, behavioural science, or related fields.

Responsibilities

Support training, fine-tuning, and evaluation of neural foundation models.
Run experiments, track results, and help 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 multimodal video, sensor, and physiological data for training.
Help keep experiment tracking, datasets, and compute usage organized.
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.