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.

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.

You 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.

We are seeking a versatile engineer with strong fundamentals, broad technical range, and the maturity to make sound trade-offs.

Required experience:

• 5+ years of software or ML engineering experience, including significant time maintaining a large production library or codebase.

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, Physics, or a related technical field — or equivalent practical experience.

• Strong Python engineering skills, with a focus on code quality, testing, and maintainability.

• Experience designing and running simulations at scale.

• Solid understanding of caching and performance optimization, including practical experience debugging memory and performance issues.

• Working knowledge of Azure Machine Learning, or willingness to ramp on Azure ML quickly from a comparable cloud ML environment.

• Strong backend fundamentals: APIs, data pipelines, testing, and CI/CD.

• Sufficient frontend proficiency to ship small UI features independently.

• Effective use of AI development tools (e.g. Claude) as part of your daily workflow to accelerate development, review, and debugging.

• Strong communication skills in English, with the ability to explain complex technical concepts to both technical and non-technical stakeholders within the team.

Mindset and ways of working:

• Accountability. Ownership of outcomes, not only of tasks.

• End-to-end thinking. Awareness of how technical decisions affect the full product experience.

• Adaptability. Comfort operating in a product-market-fit phase where priorities evolve.

• Collaboration. A constructive, low-ego working…