Role overview

As a Senior Machine Learning Engineer at Merqato, you will lead the end‑to‑end design, delivery, and operation of forecasting systems that power our commercial MVP and subsequent products. You will shape technical strategy and vision with the Product Lead, mentor engineers, and partner with domain experts to turn messy, signals into reliable decisions for our customers.

You have a proven track record scaling data products from zero to meaningful revenue impact in an early-stage environment (e.g., supporting a business growing to ~€10M in revenue). You bring strong software engineering and machine learning depth, and you can design architectures that allow teams and systems to scale safely as the product, customer base, and data volumes grow. This role is an individual contributor role with clear growth toward technical leadership and potentially tech lead responsibilities.

Key responsibilities

• Own forecasting systems end to end: Design, implement, evaluate, deploy, and operate time‑series and causal forecasting models for supply, demand, and pricing.
• Technical leadership: Set ML best practices, mentor peers, lead design reviews, and raise the engineering bar across data, modeling, and MLOps.
• Has experience with building infrastructure for ML at scale: Build robust pipelines for data ingestion, feature stores, training, validation, and CI/CD for models. Automate retraining and rollout with canaries and A/Bs to build for scalability.
• Reliability and observability: Implement explainability, bias checks, data and model drift monitoring, alerting, and SLAs for model quality and latency.
• Analytics for product impact: Instrument product analytics and define success metrics to guide roadmap decisions and quantify business value.
• Architecture and infra: Design future‑proof, cloud‑native ML architectures on Azure that enable fast iteration and safe deployments.
• Collaboration: Work closely with product, engineering, and customers to translate business goals into measurable ML outcomes.
• Technology radar: Stay current with the ML/AI ecosystem and introduce the right tools and approaches to maximize impact, not hype.

Qualifications

• 8+ years of experience: Relevant experience in machine learning engineering, data products, and production software systems.
• Deep forecasting experience: Proven track record building and deploying forecasting models to production across multiple techniques and horizons.
• Software + ML engineering excellence: Strong software engineering fundamentals (design, testing, code quality) and ML engineering practices (pipelines, CI/CD, monitoring). Comfortable owning services in production.
• Scaling data products: Demonstrated experience taking a data product from 0→1 and scaling it to support a business reaching ~€10M in revenue (or comparable scale and complexity). Able to connect technical work to commercial outcomes.
• Architecture for growth: Experience designing robust, cloud-native architectures and data platforms that scale with increasing customers, data volumes, and model complexity.
• Time‑series: Hands‑on with feature engineering for temporal data, uncertainty estimation, backtesting, and model explainability for stakeholders.
• Data visualization: Ability to turn complex time‑series outputs into clear, actionable insights and product‑ready visuals.