Backend Software Engineer – Python & ML

Overview
The Software Engineer is responsible for building and maintaining the backend services and APIs that the 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.
**This is a 3-month contract, with extension to 6 months based on performance.
Responsibilities
Software Design & Development
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).
Production Ownership
Deploy and operate services in production, including monitoring and triage of failures.
Diagnose issues across service boundaries, including 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.
Data, ML & Integrations
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 such as 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.
Cross-Functional Collaboration
Partner directly with business users across 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 enables business users to work independently and allows other engineers to pick up your work.
Continuous Improvement
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.
Education & Experience Required
Professional software engineering experience with backend development in Python.
Experience designing, building, and consuming APIs.
Experience applying machine learning or statistical modeling techniques, such as forecasting, predictive modeling, or other data science methods.
Experience with scientific or numerical Python, including NumPy, pandas, and related tooling.
Experience with databases, including schema design, migrations, and query performance.
Background in renewable energy development and/or operations, including solar development, energy modeling, performance engineering, or asset management.
Demonstrated ownership of software in production, including debugging and resolving live issues.
Strong written communication skills with the ability to document technical work and explain it clearly to non-technical audiences.
Judgment to distinguish problems that warrant a robust engineered solution from those that need a fast, correct answer today.
Ability to manage ambiguity and drive work to completion with limited direction.
Preferred Qualifications
Experience with cloud infrastructure (AWS) and container orchestration (Kubernetes).
Working knowledge of containers and CI/CD.
Experience integrating with Salesforce or a comparable enterprise system of record.
Familiarity with energy modeling.
Experience with ML frameworks and libraries such as scikit-learn, PyTorch, or TensorFlow, or with time-series forecasting methods.
Ability to embrace and work in alignment with the organization's mission and values.

Work arrangement
Yes