About Us With electric vehicles expected to be nearly 30% of new vehicle sales by 2025 and more than 50% by 2040, electric mobility is becoming a reality. ChargePoint (NYSE: CHPT) is at the center of this revolution, powering one of the world’s leading EV charging networks and a comprehensive set of hardware, software and mobile solutions for every charging need across North America and Europe. We bring together drivers, businesses, automakers, policymakers, utilities and other stakeholders to make e-mobility a global reality. Since our founding in 2007, ChargePoint has focused solely on making the transition to electric easy for businesses, fleets and drivers. ChargePoint offers a once-in-a-lifetime opportunity to create an all-electric future and a trillion-dollar market. At ChargePoint, we foster a positive and productive work environment by committing to live our values of Be Courageous, Charge Together, Love our Customers, Operate with Openness, and Relentlessly Pursue Awesome. These values guide how we show up every day, align, and work together to build a brighter future for all of us. Join the team that is building the EV charging industry and make your mark on how people and goods will get everywhere they need to go, in any context, for generations to come. Reports To Director, NOC Delivery & Automation What You Will Be Doing We’re looking for a Software Intern to Test Automation Engineer to lead quality engineering across two high-impact initiatives: our Voice AI, Monitoring & Analytics Platform. What You Will Bring to ChargePoint Design and own the overall test automation framework and strategy across both Voice AI and Monitoring & Analytics platforms Define test pyramid principles - unit, integration, contract, E2E and enforce them across repositories Lead architecture decisions for automation tooling, CI/CD test pipelines, and observability of test health Establish standards for test data management, environment parity, and flakiness elimination Own the performance testing practice - load, stress, soak, and spike testing for backend services and APIs Define performance baselines, SLOs, and regression gates integrated into CI/CD Requirements Strong programming skills in Python Basic knowledge of statistics and linear algebra Hands-on experience with machine learning libraries such as TensorFlow, PyTorch, or scikit-learn Understanding of core ML concepts including supervised/unsupervised learning, neural networks, and model evaluation metrics Familiarity with data manipulation using Pandas and NumPy Familiarity with computer vision frameworks and techniques (OpenCV, CNNs, YOLO, or torchvision) Knowledge of diffusion models (Stable Diffusion, DALL-E) or GAN architectures Hands-on experience with LLMs (GPT, Claude, Llama) and prompt engineering Experience with cloud platforms (AWS, GCP, Azure) for ML workloads Knowledge of MLOps practices including model versioning, experiment tracking (MLflow, Weights & Biases), and deployment Published research or personal projects demonstrating ML expertise Participation in Kaggle competitions or similar challenges Educational Qualifications Currently pursuing B.Tech/M.Tech in Computer Science, Data Science, Artificial Intelligence, Mathematics, or related field No active backlogs at the time of application