Senior Machine Learning Engineer - Forecasting Platform

About INAIT INAIT is a Swiss deep-tech AI company headquartered in Lausanne, building on more than 20 years of scientific research to develop a differentiated class of artificial intelligence. We are now in commercialization-scaling mode, focused on AI forecasting, and accelerating our go-to-market through a strategic partnership with Microsoft that covers joint product development, co-selling, and Azure-based deployment.  About Future Complete Future Complete is an API-first forecasting platform. We build self-service forecasting models that deliver rigorous predictions in fast-moving environments, across multiple verticals. We have run a series of successful proofs of value with target customers and are now in the pilot phase, finalising our product-market fit ahead of a significant scale-up. Our ambitions are high, and the next engineer we hire will have a lasting impact on the architecture and quality of the platform.  Our team is composed of software engineers, infrastructure engineers, and data scientists working closely together on a shared roadmap.  The RoleYou will be responsible for the long-term health, performance, and reliability of our forecasting libraries as we scale. The role is end-to-end: from the mathematical components inside the models to the user-facing functionality they enable.  This is a hybrid role based in Lausanne, Switzerland (2 days/week in office), or fully remote within Europe with working hours overlapping CET and occasional travel to Lausanne. Your responsibilities will include:  • Owning and evolving our forecasting libraries — the production Python codebase that runs simulations, time-series models, and probabilistic forecasts at scale. 

• Designing for scale. Caching strategies, multi-threading, asynchronous pipelines, and memory-efficient simulations to ensure the platform performs reliably as load grows significantly. 

• Building on Azure Machine Learning. Pipelines, compute, model registry, and deployment — Azure Machine Learning is the production platform our forecasting workloads run on. 

• Working across the stack. Primarily backend, with frontend contributions when product requirements call for it. 

• Partnering with our data scientists to translate research-grade models into reliable, production-ready components. 

• Setting the technical bar for engineers we will hire as we scale — through code review, design, and the standards you establish. 

• Contributing to the technical roadmap. As our product evolves, priorities will shift. We expect strong technical judgment and a willingness to adjust direction when the data supports it.