Junior AI/ML Engineer

Job Description

Type: Full-time Entry Level Role Start date: ASAP Location: SF, USA, remote — On-site/hybrid possible The successful candidate must be eligible to work in the US or the UK. 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 Work with biomechanical data, machine vision data, and robotics control problems 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) Experience with machine vision or robotics problems 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.