Junior Platform Engineer

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 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-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 - Information Security

At ChargePoint, we are building the future of electric mobility. As a Junior Platform Engineer, you will play a critical role in integrating advanced AI and machine learning capabilities into our platform infrastructure. You will help drive innovation by prototyping, testing, and deploying intelligent models that enhance platform security, efficiency, and the overall EV charging experience.

What You Will Be Doing

In this role, you will work closely with senior engineering and cross-functional teams to build and maintain data pipelines and machine learning integrations. Your day-to-day will involve writing clean Python code, developing APIs, and automating processes to transition AI solutions from experimentation into staging and production environments. Success in this position means effectively bridging the gap between AI development and platform engineering to deliver scalable, intelligent features.

What You Will Bring to ChargePoint

Assist

in developing, training, and fine-tuning machine learning, deep learning, and NLP models.

Support

senior engineers in designing, building, and maintaining robust data pipelines.

Implement

foundational preprocessing, feature engineering, and data validation processes.

Contribute

to testing, evaluating, and optimizing model performance for scalability and accuracy.

Deploy

machine learning models into staging and production environments under the guidance of senior team members.

Write

clean, modular, and well-documented Python code for AI experiments and platform prototypes.

Develop

APIs, model inference scripts, and automate small-scale tasks to streamline engineering workflows.

Collaborate

with cross-functional teams to translate business requirements into technical deliverables.

Research

and stay continuously updated with emerging AI tools, frameworks, and MLOps techniques.

Requirements

• 1+ years of experience in AI/ML engineering, platform engineering, or relevant academic/internship projects.
• Bachelor's degree in Computer Science, Data Science, Engineering, or equivalent practical experience.
• Solid programming skills in Python and foundational ML libraries (e.g., NumPy, Pandas, scikit-learn).
• Basic understanding of deep learning frameworks such as PyTorch or TensorFlow.
• Exposure to Natural Language Processing (NLP), Large Language Models (LLMs), or computer vision projects.
• Understanding of data structures, algorithms, and model evaluation metrics.
• Strong analytical problem-solving skills and a highly proactive willingness to learn new technologies.

Preferred Qualifications

• Hands-on experience with end-to-end ML projects, hackathons, or applied research work.
• Familiarity with cloud platforms (AWS, Azure, or GCP).
• Knowledge of version control (Git), containerization (Docker), or basic MLOps concepts.
• Understanding of vector databases, embeddings, or Retrieval-Augmented Generation (RAG) architectures.

Location

Bangalore / Gurgaon / Remote

We are committed to an inclusive and diverse team. ChargePoint is an equal opportunity employer. We do not discriminate based on race, color, ethnicity, ancestry, national origin, religion, sex, gender, gender identity, gender expression, sexual orientation, age, disability, veteran status, genetic information, marital status or any legally protected status.

If there is a match between your experiences/skills and the Company needs, we will contact you directly.

ChargePoint is committed to fostering an inclusive workplace that welcomes and supports all qualified individuals. In alignment with this commitment, we ensure that persons with disabilities are provided with reasonable accommodations throughout the employment process.

If you need a reasonable accommodation to participate in the application or interview process, to perform essential job functions, or to access any other benefits and privileges of employment, please contact us at

[email protected]

.

ChargePoint is an equal opportunity employer.
Applicants - Recruiting agencies do not contact.

• 1+ years of experience in AI/ML engineering, platform engineering, or relevant academic/internship projects
• Bachelor's degree in Computer Science, Data Science, Engineering, or equivalent practical experience
• Solid programming skills in Python and foundational ML libraries (NumPy, Pandas, scikit-learn)
• Basic understanding of deep learning frameworks (PyTorch or TensorFlow)
• Exposure to NLP, LLMs, or computer vision projects
• Understanding of data structures, algorithms, and model evaluation metrics
• Strong analytical problem-solving skills and willingness to learn new technologies
• Hands-on experience with end-to-end ML projects, hackathons, or applied research work
• Familiarity with cloud platforms (AWS, Azure, or GCP)
• Knowledge of version control (Git), containerization (Docker), or basic MLOps concepts
• Understanding of vector databases, embeddings, or RAG architectures