Application Guide

How to Apply for Staff AI Engineer

at Charge Point

🏢 About Charge Point

ChargePoint operates the world's largest open EV charging network, uniquely positioned at the intersection of clean energy, transportation, and technology. Working here means contributing directly to the global transition to electric vehicles while solving complex real-world problems at massive scale across 80+ countries.

About This Role

As a Staff AI Engineer at ChargePoint, you'll lead development of production AI systems across Voice AI, Computer Vision, and Conversational AI domains that directly impact millions of EV drivers and operators. This role involves architecting solutions that enhance monitoring platforms, improve customer support, and enable intelligent automation across charging infrastructure.

💡 A Day in the Life

A typical day involves collaborating with product and hardware teams to design AI features, architecting scalable solutions using FastAPI and LangChain, reviewing AI model performance across the charging network, and mentoring engineers on best practices for production AI systems. You'll balance technical leadership with hands-on development of features that directly impact EV driver experience and network reliability.

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🎯 Who Charge Point Is Looking For

  • 8+ years software engineering experience with 4+ years specifically building production AI/ML systems that handle real-world data at scale
  • Deep hands-on experience with LLM engineering stack: LangChain/LangGraph, Amazon Bedrock/OpenAI APIs, prompt engineering, and RAG architectures for conversational AI applications
  • Expertise in Python backend frameworks (FastAPI/Django) for deploying AI services that integrate with EV charging infrastructure and monitoring systems
  • Strong background in either Computer Vision for monitoring/analytics or Voice AI systems, with ability to lead development across multiple AI domains

📝 Tips for Applying to Charge Point

1

Highlight specific production AI systems you've built that handled real-time data or customer-facing applications, not just research projects

2

Demonstrate experience with the exact tech stack mentioned: Python, FastAPI/Django, LangChain/LangGraph, and cloud AI services (AWS Bedrock preferred)

3

Show how your AI experience relates to physical infrastructure, IoT, or large-scale networks - ChargePoint's AI works with EV charging hardware

4

Quantify impact of previous AI projects in terms of scale (users served, data processed) and business outcomes, not just technical metrics

5

Tailor your resume to show progression from software engineering to AI specialization, emphasizing the 4+ years of production AI experience requirement

✉️ What to Emphasize in Your Cover Letter

["Explain why you're passionate about applying AI to accelerate EV adoption and sustainable transportation", 'Provide concrete examples of leading AI projects from concept to production deployment, especially those involving cross-functional teams', 'Demonstrate understanding of how AI can improve both customer experience (drivers) and operational efficiency (charging network operators)', 'Highlight experience with the specific AI domains mentioned: Voice AI, Computer Vision, or Conversational AI applications']

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Study ChargePoint's product ecosystem: mobile app, station hardware, and cloud services to understand where AI fits
  • → Research the challenges of EV charging networks: reliability, user experience, maintenance, and grid integration
  • → Understand the competitive landscape in EV charging and how AI could provide differentiation
  • → Explore ChargePoint's existing AI/ML initiatives through their blog, press releases, or engineering talks

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 Architecting scalable AI systems for monitoring thousands of EV charging stations with real-time data processing
2 Designing RAG systems or conversational AI for customer support around charging issues and technical problems
3 Implementing computer vision solutions for station monitoring, maintenance detection, or user experience improvements
4 Deploying and maintaining production AI services using FastAPI/Django with considerations for reliability in critical infrastructure
5 Leading cross-functional collaboration between AI, hardware, and operations teams in an IoT environment
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⚠️ Common Mistakes to Avoid

  • Focusing only on academic/research AI experience without demonstrating production deployment at scale
  • Generic AI knowledge without specific examples using the required stack (LangChain, FastAPI, AWS Bedrock/OpenAI)
  • Applying with a resume that doesn't clearly show the 8+ years software engineering and 4+ years production AI experience progression

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

Congratulations!

Ready to Apply?

Good luck with your application to Charge Point!

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