Responsibilities
- 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.
Basic qualifications
- 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.
- 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.
Preferred qualifications
- 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.).
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.
Tags & focus areas
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