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 Product lead, mentor engineers, and partner with product and domain experts to turn messy, real‑world signals into reliable decisions for our customers. You have proven experience with building scalable of data platforms and keen to growth into leadership role.

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

• Deep forecasting experience (6+ years): Proven track record building and deploying forecasting models to production, ideally across multiple techniques and horizons.
• ML engineering excellence: Strong software engineering fundamentals, production pipelines, testing, and CI/CD for ML. Comfortable owning services in production.
• Time‑series and XAI: 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.
• Customer first mindset and strong ownership: You align ML work with outcomes, communicate trade‑offs crisply, and thrive in cross‑functional settings to deliver with high quality and on time to customers.
• Startup experience: Meaningful experience in early‑stage enterpreneurial environments, with bias to action and pragmatic delivery.

Preferred tech stack

• Languages: Python for ML, R for analysis