Applied AI Engineer

Fuse Energy is an energy startup on a mission to make energy abundant and affordable, fast. We combine first-principles thinking with cutting-edge technology to build a radically better energy system. We've raised over $200M from top-tier investors including Balderton, Lakestar, Accel, Creandum, Lowercarbon, Ribbit, 20VC, Hummingbird and Collaborative Fund, alongside strategic angels including Nico Rosberg and GPs behind Meta, Revolut, Spotify and Uber. We're building a fully integrated energy company: developing our own solar, batteries and other generation projects, building our own hardware, improving and developing grid infrastructure, trading power in real time, using AI across the business, and installing distributed energy in homes. By selling directly to consumers we cut out the middleman, lower costs and pass the savings on to our customers. We're building a cutting-edge AI team. As an Applied AI Engineer, this role suits someone with the technical depth of a backend engineer who is specifically interested in applied AI and how it can improve the energy experience for our customers and our internal operations. You'll work on consumer features such as the Energy Co-Pilot and speedy onboarding (using VLM and LLM tools) and build AI tools that make teams across Fuse more productive. Responsibilities • Design, 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) • Build and optimise internal AI tools that make the whole company more productive, with a focus on automation and enhancing workflows • Collaborate with backend engineers and data scientists to integrate AI-driven features into our platforms • Collaborate with the trading and operations teams to ensure AI models are aligned with real-time market conditions and energy pricing • Improve AI models to optimise trading strategies by anticipating market shifts based on weather and demand forecasts • Stay up to date with the latest advancements in applied AI and machine learning and apply them to real-world problems in the energy space • Monitor the performance of AI tools and models, ensuring they run efficiently and effectively Requirements • Minimum 3 years of engineering experience • Proven experience as a backend engineer with a strong interest and practical experience in applied AI or machine learning • Strong programming skills in Python (or similar) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.) • Experience working with large-scale models (LLMs/VLMs) and deploying AI-driven solutions into production • Solid understanding of cloud technologies, containerisation and building scalable AI applications • Ability to integrate AI/ML models into real-world applications, focusing on usability and performance • Strong problem-solving skills and a practical approach to implementing AI solutions in a fast-paced environment • Experience working with large datasets, particularly in relation to demand and supply forecasting • Bonus: experience or strong interest in energy markets and trading strategies; understanding of weather forecasting, energy demand patterns and production modelling; exposure to NLP or related fields Benefits • Competitive salary and eligibility for equity • Biannual bonus scheme • Fully expensed tech to match your needs • Private health insurance • Breakfast and dinner allowance for office-based employees As we hire globally, benefits may vary by location. Show more Show less

Minimum 3 years of engineering experience. Proven experience as a backend engineer with a strong interest and practical experience in applied AI or machine learning. Strong programming skills in Python (or similar) with familiarity in AI/ML libraries (TensorFlow, PyTorch, etc.). Experience working with large-scale models (LLMs/VLMs) and deploying AI-driven solutions into production. Solid understanding of cloud technologies, containerisation and building scalable AI applications. Ability to integrate AI/ML models into real-world applications, focusing on usability and performance. Strong problem-solving skills and a practical approach to implementing AI solutions in a fast-paced environment. Experience working with large datasets, particularly in relation to demand and supply forecasting. Bonus: experience or strong interest in energy markets and trading strategies; understanding of weather forecasting, energy demand patterns and production modelling; exposure to NLP or related fields.

Design, 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). Build and optimise internal AI tools that make the whole company more productive, with a focus on automation and enhancing workflows. Collaborate with backend engineers and data scientists to integrate AI-driven features into our platforms. Collaborate with the trading and operations teams to ensure AI models are aligned with real-time market conditions and energy pricing. Improve AI models to optimise trading strategies by anticipating market shifts based on weather and demand forecasts. Stay up to date with the latest advancements in applied AI and machine learning and apply them to real-world problems in the energy space. Monitor the performance of AI tools and models, ensuring they run efficiently and effectively.