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 style in a small senior team.
• Drive. A consistent willingness to go beyond the minimum requirement of the role.

Nice to have:

• Hands-on experience with forecasting and time-series models (classical, machine-learning-based, or both).
• Experience with multi-threading and concurrency, including debugging race conditions at scale.
• Experience in finance, energy, retail, or another domain where forecasting drives material business decisions.
• Open-source contributions to the scientific Python ecosystem (pandas, scikit-learn, statsmodels, etc.).

We welcome applications even if you do not meet every requirement listed above. We value range, judgment, and a strong drive to build — if the role excites you, we would like to hear from you.

• Competitive compensation plus a performance bonus tied to the commercial outcomes we deliver as a company.
• Eligibility to our long-term incentive plan (phantom stock program) in a company at an inflection point.
• Hybrid working model for our Lausanne-based team (2 days per week in the office), or fully remote within Europe.
• Relocation package for candidates moving to Switzerland.
• Senior scope. Ownership of systems and decisions, not isolated tickets.
• A cohesive team. Engineering, infrastructure, and data science working as one group, with direct access to founders.
• Product-market fit phase and a clear scaling plan. The foundational work is done; the next phase is growth.
• Fresh fruit, snacks, and drinks at the office.