Senior ML Engineer – Cloud Cost Optimization/FinOps

At The Global Talent Co., we connect top global talent with leading technology companies worldwide. This position is a direct hire with BLOCKS, a Berlin-based startup revolutionizing cloud cost optimization. The initial contact will be with The Global Talent Co., but your employment and day-to-day work will be directly with BLOCKS.

About the Company

BLOCKS is a Berlin-based startup revolutionizing cloud cost optimization for startups and SMEs. Combining a group-buying model with its AI agent “Major Tom,” BLOCKS guarantees 20% savings from day one through automated infrastructure efficiency. With strong funding, rapid growth, and an AI-native engineering culture, BLOCKS empowers small, autonomous teams to create outsized impact.

About the Job

As a Senior ML Engineer, you’ll design and deploy machine learning systems that power BLOCKS’ cloud cost optimization platform. Your focus will be on predictive modeling, anomaly detection, and cost optimization algorithms that ensure measurable savings for customers.

You’ll work with large-scale AWS billing data, build real-time forecasting systems, and design ML models that predict usage, detect inefficiencies, and automate optimization strategies. This is a high-impact, production-focused role where your models directly influence millions in cloud spend and contribute to scaling BLOCKS to manage €500M+ in cloud volume.

What You’ll Do

• Develop ML models for time series forecasting of cloud usage, seasonality, and trends.
• Implement anomaly detection systems for real-time monitoring of cloud cost anomalies.
• Design and train clustering and classification models (K-means, decision trees, customer segmentation).
• Build predictive algorithms for Savings Plans and Reserved Instances optimization.
• Analyze AWS billing data (Cost Explorer API, CloudWatch metrics, CUR) to drive FinOps insights.
• Deploy and monitor production ML systems for real-time inference and automated alerting.
• Collaborate with engineering teams to integrate ML-driven optimizations into Major Tom’s decision engine.

What You Bring

Must-Have (Non-negotiable)

• Strong Python ML expertise: scikit-learn, pandas, numpy.
• Proven experience with time series forecasting (Prophet, statsmodels).
• Anomaly detection expertise: statistical methods, outlier detection, alerting.
• Solid background in clustering & classification (K-means, hierarchical clustering, decision trees).
• Hands-on experience with AWS billing systems: Cost Explorer API, CloudWatch metrics, billing data analysis.
• Production ML systems experience: deployment, monitoring, real-time inference.

Strong Plus

• Multi-cloud platforms: GCP Billing API, cross-cloud cost optimization.
• MLOps infrastructure: MLflow, model versioning, CI/CD for ML, automated retraining.
• Financial modeling & FinOps principles: ROI optimization, cost-performance trade-offs.
• Real-time systems: stream processing, low-latency predictions, event-driven architectures.
• Optimization algorithms: linear programming, constraint optimization, operations research.
• Data engineering: Spark, time series databases, large-scale data pipelines.
• Cloud architecture knowledge: RIs, Savings Plans, rightsizing strategies.

Why Join BLOCKS

• Zero to One: The opportunity to build a category-defining product from scratch.
• True ownership: Small teams, high autonomy, fast learning cycles, and zero politics.
• Top-of-market compensation: Competitive salary and participation in a virtual stock option program.
• Relocation support: Assistance provided for candidates relocating to Berlin.