Job title: Machine Learning Engineer

Location: London, Bristol, Edinburgh (including Hybrid)

Salary: £63,200 - £86,900

Reporting to: Head of Data Science & Products

This role is based in the UK and requires existing right to work in the UK.

At this time, we are not able to offer visa sponsorship for this role. We are committed to building a diverse, global team and our sponsorship policy is evaluated on a role-by-role basis. We encourage you to keep an eye on our careers site to stay informed about future opportunities where we are able to offer visa sponsorship.

Kaluza is the Energy Intelligence Platform, turning energy complexity into seamless coordination. We help energy companies overcome today’s challenges while accelerating the shift to a clean, electrified future.

Our platform orchestrates millions of real-time decisions across homes, devices, markets and grids. By combining predictive algorithms with human-centred design, Kaluza makes clean energy dependable, affordable and adaptive to everyday life.

With teams across Europe, North America, Asia and Australia, and a joint venture with Mitsubishi Corporation in Japan, we power leading companies including OVO, AGL and ENGIE, as well as innovators like Volvo and Volkswagen.

At Kaluza we embrace a flexible, hybrid work model that balances autonomy with the power of in-person connection. Many of our teams find value in coming together regularly to collaborate, strengthen relationships, and accelerate progress. We’re focused on shaping thoughtful, team-driven approaches that support both business impact and individual well-being.

Where in the world of Kaluza will I be working?

You’ll be part of the centralised Kaluza ML team and wider Data community where you’ll share knowledge, support other MLEs, Analysts and Product teams. You’ll be developing optimisation, ML algorithms and GenAI solutions across Kaluza.

What will I be doing?

Data is the foundation of everything we do, and to deliver our vision we need curious, tenacious people who can turn this data into strategy and actions with their expertise.

As an MLE at Kaluza, you’ll help product teams identify patterns and solve challenges with data. Projects include Forecasting, Recommenders and HelpDesk ticket classification.

Key responsibilities include:

• Develop ML and GenAI Solutions: Design and implement machine learning using Python, leveraging data technologies such as Databricks, Kafka, and the AWS cloud environment. Our architecture is based on microservices, allowing for dynamic deployment of new features.
• Productionise Algorithms: Deploy algorithms into production environments where they can be tested with customers and continuously improved. You’ll automate workflows, monitor performance, and maintain data science products using best practices for tooling, alerting, and version control (e.g., Git).
• Contribute to a Collaborative Data Science Culture: Share your knowledge and experience with the wider team. You’ll play a key role in fostering an ML / AI community that values openness, collaboration, and innovation.
• Identify Opportunities for Impact: Help uncover new opportunities where ML/AI can add value across our products and services. This includes asking the right questions, identifying high-impact areas, and contributing to the broader data strategy.

Ideally you will have:

• Proven experience in a real-world ML / AI role, with strong understanding of core algorithms, data structures, and model performance evaluation.
• Proficiency in Python, including libraries such as Scikit-learn, Pandas, NumPy, and others commonly used in the ML ecosystem.
• Hands-on experience with GenAI APIs and tools, including deployment and integration of GenAI solutions into production systems.
• Strong analytical and problem-solving skills, with the ability to approach complex problems methodically while keeping business impact in mind.
• Experience across the full ML lifecycle, including data preprocessing, model training, evaluation, deployment, and monitoring in production environments.
• Experience with MLOps tools and practices (e.g., MLflow, SageMaker, Docker, CI/CD pipelines)
• Excellent communication and presentation skills, capable of clearly articulating technical results to both technical and non-technical stakeholders, including senior leadership.
• Track record of stakeholder engagement, collaborating cross-functionally with product, engineering, and business teams.
• Solid foundation in statistics, including techniques such as hypothesis testing, significance testing, and probability theory.
• Comfortable working in an agile environment, contributing to iterative development cycles and cross-functional teams.
• Some experience with Scala is a plus