AI/Machine Learning Engineering Intern

Join Gecko Robotics, a company that collects rich inspection data from wall-climbing robots. As an AI/ML Engineering Intern, you will work on a defined project that turns data into decisions, building models and systems that run reliably on real customer data. You will own a scoped machine learning project, build data pipelines, train and evaluate models, integrate workflows, investigate failure modes, and present your project at the end of the internship. This is an engineering internship focused on building systems that analyze data.

• Participer à un projet défini qui transforme les données d'inspection en décisions, en construisant des modèles et les systèmes qui les entourent.
• Construire des pipelines de données en Python qui nettoient, étiquettent et structurent les données des capteurs pour l'entraînement et l'inférence des modèles.
• Former, évaluer et itérer sur les modèles, et concevoir des évaluations qui reflètent comment la sortie sera utilisée sur le terrain.

• You are an engineer who happens to work in machine learning
• You care whether a model works on real data, not just on a benchmark, and you are comfortable owning the code around the model: the pipeline, the evaluation, and the integration
• You approach new problems methodically, communicate clearly about results and uncertainty, and are excited to be the first person to look at a problem
• Strong programming experience in Python, including writing well-structured, tested code beyond notebooks
• Ability to work onsite in New York, Monday through Friday, for the full Summer 2027 internship
• Currently pursuing a bachelor’s, master’s, or doctoral degree in computer science, electrical engineering, applied mathematics, physics, or a related technical discipline, with an expected graduation date between December 2027 and May 2028
• Working familiarity with Git or similar version control systems
• Working understanding of linear algebra, probability, and statistics
• Ability to independently close knowledge gaps, stay organized, and learn from feedback
• Coursework or project experience in machine learning, including training and evaluating models on real datasets
• Strong written and verbal communication skills, including the ability to explain results, quantify uncertainty, and collaborate on ambiguous problems
• Graduate-level coursework or research in machine learning, signal processing, or computer vision
• Experience with LLM APIs, retrieval, information extraction, or document understanding
• Experience taking a model beyond a notebook: building a pipeline, an API, or a tool around it
• Experience with time-series or sensor data, especially ultrasonic, acoustic, or other physical measurements
• Interest in industrial, defense, or infrastructure applications where reliability matters
• Exposure to cloud infrastructure, preferably GCP, and to containers such as Docker