About the company
Our mission is to make cheap renewable energy accessible for everyone. We have raised $78m from top tier investors like Balderton, Lakestar, Accel, Creandum, Lowercarbon, and Ribbit. At Fuse, we believe in transparency ā what you see is what you get. There are no hidden games, just hard work, problem-solving, and high quality output. Our workplace is a low-ego environment, where we help each other deliver great outcomes. We debate the best path forward, and then execute with precision. We prize a can-do attitude and a strong willingness to learn on a daily basis. We never settle ā second best is not an option.
Job Summary
Requirements
šDesign, develop, and deploy AI-powered features that directly impact consumer experiences, including personalised energy recommendations and seamless onboarding via AI models (e.g., using energy bills for quick setup). šBuild and optimise internal AI tools that will make the whole company more productive, with a focus on automation and enhancing workflows. šCollaborate with backend engineers and data scientists to integrate AI-driven features into our platforms. šContinuously improve and optimise AI models (including LLM and VLM) to provide a better user experience. šDevelop scalable, maintainable AI infrastructure to support a growing set of consumer-facing and internal AI features. šCollaborate with the trading and operations teams to ensure the AI models are aligned with real-time market conditions and energy pricing. šImprove AI models to optimise trading strategies by anticipating market shifts based on weather and demand forecasts. šStay up to date with the latest advancements in applied AI and machine learning, and apply them to solve real-world problems within the energy space. šMonitor the performance of AI tools and models, ensuring they are functioning efficiently and effectively.
Skills & Qualifications:
šProven experience as a Backend Engineer with a strong interest and practical experience in applied AI or machine learning. šStrong programming skills in Python (or similar languages) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.). šExperience working with large-scale models (LLM/VLM) and deploying AI-driven solutions into production. šSolid understanding of cloud technologies, containerization, and building scalable AI applications. šAbility to integrate AI/ML models into real-world applications, focusing on usability and performance. šStrong problem-solving skills and a practical approach to implementing AI solutions in a fast-paced environment. šFamiliarity with cloud-based platforms (AWS is a plus) and services related to AI/ML is a plus.
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