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Selected role

Senior Machine Learning Engineer - Forecasting Platform

INAIT Lausanne, VD, CH

Senior Full-time

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 Role

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.

Requirements

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.

Benefits

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.

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