Qcells North America is seeking an exceptional Staff Software Engineer to lead the design and development of software products that manage and optimize distributed energy resources. The role focuses on building scalable, production-grade energy software while collaborating with cross-functional teams across data, hardware, and energy markets.
Responsibilities:
- Architect and develop high‑performance, cloud‑native software for managing DER, BTM and C&I assets, and utility‑scale energy systems
- Build robust microservices, APIs, and distributed systems supporting real-time or near-real-time energy operations
- Use modern AI‑assisted software development tools to improve velocity, code quality, and reliability
- Develop modeling and orchestration logic for DER, BESS, PV+BESS hybrid systems, demand flexibility, and VPP aggregation and grid‑service participation
- Collaborate with hardware teams to integrate software features in Edge Dispatcher with inverters, batteries, EMS/SCADA systems, and edge controllers
- Work with data/ML teams to incorporate forecasting, predictive maintenance, dispatch logic, and optimization modules
- Improve reliability, fault tolerance, and scalability of large energy portfolios
- Mentor engineers and drive architectural decisions
Requirements:
- Bachelor's, Master's, or PhD in Computer Science, Electrical Engineering, or related field
- 8+ years of experience building production-grade software platforms
- Experience in DER, BTM/C&I asset management, utility‑scale storage systems, or VPPs
- Strong programming skills in Python, Java, or C++
- Cloud-native development experience (AWS/GCP/Azure), microservices, distributed systems
- Hands-on experience with AI-assisted development tools
- Familiarity with BESS/DER telemetry, distributed energy protocols, and grid integration
- Travel may be required up to 10%, depending on business needs
- Experience building or operating VPP, DERMS, or EMS platforms
- Knowledge of energy markets: DR, FR, capacity, arbitrage
- Experience with CI/CD, IaC, containers, observability tooling
- Background integrating forecasting, AI, or ML models