What We Do
Gecko Robotics is helping the world’s most important organizations ensure the availability, reliability, and sustainability of critical infrastructure. Gecko's complete and connected solutions combine wall-climbing robots, industry-leading sensors, and an AI-powered data platform to provide customers with a unique window into the current and future health of their physical assets. This enables real-time decision making to increase the efficiency and safety of operations, promote mission readiness, and protect the environment and civilization from the effects of infrastructure failure.
Role at a Glance
Gecko Robotics is seeking an AI/ML Engineering Intern to join our Engineering team in New York for Summer 2027. This is a full-time, onsite internship, Monday through Friday.
Gecko collects some of the richest inspection data in the world: ultrasonic and other non-destructive testing (NDT) measurements from wall-climbing robots, paired with the documents, drawings, and operational records that describe the assets they inspect. You will work on a defined project that turns that data into decisions, building models and the systems around them that run reliably on real customer data. This is an engineering internship, not an analytics one. We build the systems that analyze the data, not just the analysis.
Due to government program requirements, candidates must be authorized to work in the United States.
What you will do
Own a scoped machine learning project from problem framing through evaluation and integration, under the guidance of experienced engineers.
Build data pipelines in Python that clean, label, and structure NDT sensor data, documents, or operational records for model training and inference.
Train, evaluate, and iterate on models, and design evaluations that reflect how the output will be used in the field.
Integrate a model or LLM-based workflow into a service or tool that other engineers and Forward Deployment Engineers can use.
Investigate failure modes on messy, real-world data, document what you find, and recommend what to fix next.
Use version control, code review, testing, and experiment tracking to produce work others can reproduce and build on.
Present your project, results, and recommended next steps at the end of the internship.
Technologies We Use
Python, PyTorch or similar ML frameworks, NumPy and pandas, SQL, LLM APIs and agent frameworks, Git, Linux, and cloud infrastructure (GCP). Depending on the project, you may also work with signal-processing tools for ultrasonic data, document-processing and information-extraction pipelines, or orchestration tools such as Airflow. The specific tools will depend on the assigned project.
About You
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.
Required Skills
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.
Ability to work onsite in New York, Monday through Friday, for the full Summer 2027 internship.
Strong programming experience in Python, including writing well-structured, tested code beyond notebooks.
Coursework or project experience in machine learning, including training and evaluating models on real datasets.
Working understanding of linear algebra, probability, and statistics.
Working familiarity with Git or similar version control systems.
Strong written and verbal communication skills, including the ability to explain results, quantify uncertainty, and collaborate on ambiguous problems.
Ability to independently close knowledge gaps, stay organized, and learn from feedback.
Preferred Skills
Graduate-level coursework or research in machine learning, signal processing, or computer vision.
Experience taking a model beyond a notebook: building a pipeline, an API, or a tool around it.
Experience with LLM APIs, retrieval, information extraction, or document understanding.
Experience with time-series or sensor data, especially ultrasonic, acoustic, or other physical measurements.
Exposure to cloud infrastructure, preferably GCP, and to containers such as Docker.
Interest in industrial, defense, or infrastructure applications where reliability matters.
Who We Are
At Gecko, our people are our greatest investment. In addition to competitive compensation packages, we offer company equity, 401(k) matching, gender-neutral parental leave, full medical, dental, and vision insurance, mental health, ongoing professional development, family planning assistance, and flexible paid time off.
Gecko values collaboration, innovation, and partnership, and we believe we do our best work when we're together in person. We’re an office-first culture but understand that sometimes you may need to work from home. Many people are in the office five days a week, others need a bit more flexibility. Ultimately, we care about the outcomes we achieve - and creating a culture of autonomy and trust that enables that impact.
Gecko is committed to creating a culture of inclusion and belonging, and we are proud to be an equal opportunity employer. We believe it is our collective responsibility to uphold these values and encourage candidates from all backgrounds to join us in our mission to protect today’s infrastructure and give form to tomorrow’s. All qualified applicants will be treated with respect and receive equal consideration for employment without regard to race, color, creed, religion, sex, gender identity, sexual orientation, national origin, disability, uniform service, veteran status, age, or any other protected characteristic per federal, state, or local law. If you are passionate about what you do and want to use your talents to support our critical mission, we’d love to hear from you.