S44 Energy builds production-grade software and services for EV charging networks worldwide. With over a decade of hands-on experience, we help operators launch, scale, and evolve their platforms using open standards, flexible architectures, and software proven in real-world deployments.

We are the original authors of CitrineOS, an open-source Charge Station Management System (CSMS) foundation built on industry standards such as OCPP, OCPI, and ISO 15118. Today, CitrineOS is hosted by Linux Foundation Energy and serves as the extensible core behind many live charging networks, giving operators long-term control without vendor lock-in.

Our work lives at the intersection of physical infrastructure and software—integrating EV chargers, energy assets like solar and battery storage, cloud platforms, and user-facing applications to power reliable, scalable charging ecosystems.

production machine learning models using real-world operational data. You will play a key role in shaping how machine learning is applied across both client engagements and core product initiatives at S44 Energy.

While at the onset you will not manage people, but you will be expected to operate with a high degree of ownership—partnering closely with software engineers, product leaders, and client stakeholders to translate real-world problems into practical, reliable ML solutions.

What You’ll Do

·        Design, build, and deploy ML models that operate in production environments

·        Work with messy, incomplete, and real-world operational data from EV charging and energy systems

·        Collaborate with engineering teams to integrate models into backend services and user-facing applications

·        Partner with client teams to understand use cases, explain model behavior, and iterate based on feedback

·        Help establish early patterns and best practices for ML development, deployment, and monitoring at S44 Energy

Must Have

·        Minimum 3–5 years of experience in data science or machine learning in applied or production settings

·        Strong Python skills and experience with common ML libraries

·        Solid grounding in statistics and applied machine learning techniques

·        Experience deploying and supporting models in production systems

·        Comfort working autonomously and making informed tradeoffs in the absence of a larger ML organization

·        Ability to communicate clearly with both technical and non-technical stakeholders

Nice to Have

·        Experience with time-series data, forecasting, or anomaly detection

·        Familiarity with energy systems, EV charging, IoT, or other physical-asset domains

·        Experience working with streaming or near–real-time data

·        Interest in model explainability, monitoring, and responsible ML practices