How to apply for Senior / Staff Software Engineer, AI Systems & Platform

About UtilityAPI

UtilityAPI is a mission-driven company unlocking energy data to accelerate the clean energy transition. They operate at the intersection of utilities, clean energy developers, and customers, bringing order to a fragmented market. Joining means shaping the future of energy with data and AI in a high-impact, remote-first environment.

About the role

As a Senior/Staff Software Engineer, you'll define and build UtilityAPI's AI-native architecture and development workflows from scratch. You'll assess existing systems, identify high-value AI opportunities, and lead initiatives from experimentation to production. This is a high-autonomy role combining hands-on engineering with mentorship and technical leadership.

A typical day

A typical day might involve reviewing existing data pipelines, prototyping an AI model for a high-impact use case, and collaborating with engineers to integrate it. You'll also mentor team members on AI best practices and help define the roadmap for AI-native development. Expect a mix of hands-on coding, architecture design, and strategic discussions.

Who UtilityAPI is looking for

• Experienced AI/ML engineer with a track record of deploying AI systems in production, ideally in data-intensive domains.
• Strong software engineering background, capable of designing scalable architectures and writing high-quality code.
• Comfortable with ambiguity and able to define technical strategy and best practices in a fast-moving environment.
• Interest in mentorship and leading by example, with excellent communication skills for cross-functional collaboration.

Tips for this application

• Highlight specific AI projects where you defined architecture and drove them to production, emphasizing measurable impact.
• Demonstrate familiarity with energy data or similar complex, regulated domains—show you understand the unique challenges.
• Emphasize your ability to work autonomously and lead initiatives without established frameworks.
• Showcase any experience mentoring engineers or building AI development practices, as this role requires bringing the team along.
• Tailor your resume to include keywords from the job description like 'AI-native architecture', 'data-intensive', and 'production systems'.

What to cover in your cover letter

In your cover letter, focus on: 1) Your vision for AI in energy data and how you'd approach building it at UtilityAPI. 2) A specific example of leading an AI project from concept to production. 3) Your experience mentoring and establishing engineering best practices. 4) Why you're excited about UtilityAPI's mission and the opportunity to shape their AI future.

Research before applying

• Explore UtilityAPI's products and APIs to understand how they currently handle utility data and where AI could enhance offerings.
• Research the US energy landscape, key players, and regulatory challenges around utility data access and privacy.
• Look into recent AI advancements in energy, such as load forecasting, anomaly detection, or customer segmentation, to speak knowledgeably.
• Check out UtilityAPI's blog, press releases, and LinkedIn to understand their culture, recent milestones, and technical stack.

Likely interview topics

Based on the job description, expect questions about:

• How would you assess UtilityAPI's current tech stack and identify where AI can add the most value?
• Describe a time you built an AI system from scratch. What architecture decisions did you make and why?
• How do you balance experimentation with reliability when deploying AI models in production?
• What strategies would you use to introduce AI-driven development cycles to a team unfamiliar with AI?
• How would you handle data privacy and security concerns when applying AI to utility data?

Common mistakes to avoid

• Focusing only on generic AI skills without connecting them to energy data or UtilityAPI's mission.
• Underestimating the importance of mentorship and cross-team collaboration—this role isn't just about coding.
• Proposing overly complex AI solutions without considering practicality, scalability, or the company's current stage.