• Develop and maintain software for our edge devices, server infrastructure, and web applications. • Build and improve the APIs connecting our system components (edge, server, web). • Design and improve the user interfaces that customers use in our web application. • Implement, manage, and optimize data processing and machine learning inference pipelines on our servers. • Troubleshoot problems across the hardware/software stack, from edge devices through the server to the web frontend. • Collaborate with the team to design new features and solve technical challenges.

• Build robust work order execution workflows, enabling technicians to efficiently complete installations, repairs, and service calls directly from the field. • Implement intuitive tools within our Field Service mobile application that guide technicians through structured procedures, checklists, equipment tracking, and status updates. • Develop real-time capabilities that allow technicians to access job details, capture notes and photos, collect customer signatures, and seamlessly communicate updates to both dispatch teams and end customers. • Develop, in collaboration with talented designers, delightful interfaces that wow our end users.

• Work on challenging projects that leverage AI/ML to address critical business needs in areas like fraud prevention, credit risk assessment, and data-driven decision making. • Develop advanced platforms and tools to process, enrich, and optimize core company data, ensuring it’s easy and efficient to use—unlocking value for both customers and internal teams. • Design, implement, and evaluate machine learning models, applying advanced techniques to solve complex problems in a fast-paced tech landscape. • Gain experience with big data tools and technologies, including data ingestion, processing, and analysis. • Partner with experienced engineers and researchers to brainstorm solutions and contribute to innovative projects. • Share your research findings and project outcomes with the team and stakeholders.

• Own delivery for significant features or systems: clarify requirements, design the solution, execute, ship, and iterate. • Build and maintain reliable services and workflows that support AI features in production. • Define how quality is measured for your area (success metrics, test strategy, evaluation approach) and automate it wherever possible. • Run structured experiments, interpret results, and translate learnings into product improvements. • Improve operational readiness: deployment automation, monitoring/alerting, incident follow-up, and performance/cost optimizations. • Mentor teammates through design feedback, code reviews, and practical guidance.