AI Simulation Engineer

AI Simulation Engineer (m/f/d, 100%) _Location: Zurich or Munich _ABOUT SPHERE-ENERGY We are a young, dynamic and technology-driven company, committed to developing targeted AI-driven solutions that revolutionize the development of products in the broad field of engineering. Tasks ABOUT THE POSITION We are seeking the brightest minds to join our growing team as an AI Simulation Engineer. In this role, you will lead and deliver high-impact AI projects in collaboration with leading clients across different industries, and contribute to the development of our internal AI and data product portfolio. You will work hands-on with real-world multi-modal datasets, build predictive models, and develop AI-driven simulation technologies to accelerate product development. Your role will combine technical and hands-on expertise with scientific curiosity: from extracting insights and building robust AI models to shaping client solutions and guiding the evolution of our technology offerings. This is more than a job — it is an opportunity to help shape the future of our company and drive technology innovation in one of the most critical industries of our time. Requirements KEY RESPONSIBILITIES Internal Product Development Research innovative ways of solving identified market challenges by applying data-driven and physics-informed AI technologies Own development streams towards the productionalization of new simulation products/offerings Work closely with stakeholders to gather requirements and define feature priorities Hands-on Project Involvement Participate in the delivery of client projects by developing new features and act as a key sparring partner for project managers and relevant client stakeholders Oversee the processes and structures to keep internal timeline targets Technology Scouting Support the constant development of our service portfolio by identifying new technological opportunities YOUR PROFILE Master’s degree in Data/Computer Science, Applied Mathematics, Engineering, or a related field Relevant practical experience in machine learning applied to at least two different AI domains - such as large language models, computer vision, time-series modeling, etc. Strong skills in Python, and common libraries such as Pandas, Scikit-learn, or PyTorch Experience in MLOps, including model deployment, monitoring, CI/CD pipelines, experiment tracking (e.g., MLflow), as well as cloud platforms (AWS, GCP, Azure) Track record of rapid prototyping, POCs, and iterative model improvement, ideally in a client-facing environment Ability to translate ambiguous engineering problems into AI-driven solutions and evaluate the model performance, deployment complexity, latency, and cost tradeoff Collaborative mindset paired with a high sense of end-to-end ownership and initiative Curiosity to explore emerging AI technologies, architectures, and tools “NICE TO HAVE” Experience with system simulations (CFD, FEA, Thermal/Fluid dynamics, etc.) and integrating AI models into real-world engineering systems Expertise in multi-modal AI work and various architectures (e.g. transformers, multi modal encoders) or novel modalities (multi-sensor fusion, LAMs, generative design) Benefits BENEFITS Exposure to innovative technologies in real-world applications across different industries A dynamic, young and collaborative work environment that fosters innovation Opportunity to influence strategy and product roadmap of the company Hybrid working mode and flexible working hours Fair market salary + variable compensation HOW TO APPLY If you are ready to embark on a journey of innovation and contribute to the evolution of the engineering industry, please share your resume and a cover letter detailing your interest and relevant experience.