Software Engineer, Backend Developer, Machine Learning​/ ML Engineer

The Company

The energy industry is entering one of the most significant periods of growth and transformation in its history. Meeting the nation’s growing demand for reliable electricity will require new ideas, new infrastructure, and talented people committed to building for the future.

At Cypress Creek Energy, we’re meeting that challenge by developing and operating the energy infrastructure needed to power communities, support economic opportunity, and strengthen resilience. We believe our responsibility extends beyond the grid and that how we build matters just as much as what we build.

That same commitment extends to our employees. We invest in professional growth, encourage collaboration across teams, and provide opportunities to take ownership, expand your expertise, and advance your career. Our culture is grounded in safety, accountability, respect, and a shared commitment to deliver results. Join us and help meet one of the most important energy challenges of our time while building a rewarding career.

The Software Engineer is responsible for building and maintaining the backend services and APIs that Cypress Creek Energy’s business runs on, and for applying machine learning and data science techniques to the forecasting and analytical challenges the business depends on. The role spans the full lifecycle: gathering requirements from business stakeholders, designing and implementing backend services and models, shipping them through the deployment pipeline, and supporting them in production.

This role pairs deep backend software engineering with applied machine learning. The majority of the work is designing, building, and maintaining well-tested APIs and services; that work is complemented by developing and deploying ML models that improve forecasting and data-driven decision-making across the business. Engineers build technical depth in the systems and models they own while expanding their domain knowledge of the renewable energy business those systems serve.

• Design, build, test, and maintain backend services, APIs, and internal applications.
• Write clear, well-tested, maintainable code and participate actively in code review.
• Translate business requirements into technical designs.
• Contribute to the team's engineering standards, shared libraries, and services.
• Experience with event-driven or stream-processing architectures (Kafka or similar).

• Deploy and operate services in production including monitoring and triage of failures.
• Diagnose issues across service boundaries (application code, data stores, message streams, and third-party dependencies) and resolve root causes.
• Maintain and improve CI/CD pipelines and deployment configuration for the services you work on.

• Apply machine learning and statistical modeling techniques to forecasting models and other data science initiatives across the business.
• Build and maintain integrations between internal applications and enterprise systems.
• Perform exploratory data analysis, feature engineering, and model validation using scientific Python tooling (numpy, pandas, and related libraries).
• Design and evolve data models in the company's analytical data platform and write efficient queries against large datasets.
• Build and maintain data pipelines and services that ingest, transform, and serve data from internal and third-party sources.
• Move ML models from prototype to production: package, deploy, monitor, and retrain models as part of the services you own.

• Partner directly with business users (development, engineering, finance, and operations) to understand their workflows and turn ambiguous needs into working software.
• Support internal users of the applications you own, including troubleshooting unexpected results and explaining technical behavior to non-technical audiences.
• Produce and maintain documentation that lets business users work independently and allows other engineers to pick up your work.

• Identify and address technical debt, manual processes, and operational pain points in the systems you work on.
• Evaluate new tools, libraries, and approaches, and make the case for adoption where they meaningfully improve the team's output.
• Contribute to a culture of clear documentation, code quality, and engineering discipline across the team.

• 3-6 years of professional software engineering experience with backend…