Senior Machine Learning Engineer

Sotalent; Berlin; Hybrid; EUR 90,000 - 130,000; Full time

Job summary

Sotalent is hiring a Senior Machine Learning Engineer in Berlin to design, deploy, and optimise large-scale ML systems for logistics, forecasting, and pricing. You will build production-ready ML solutions, real-time data pipelines, and cloud-based services that support millions of daily transactions.

You will work in a collaborative Data Science and Engineering environment, focusing on scalable ML applications, real-time streaming, and cloud-native architectures.

Qualifications

• Strong professional experience in Machine Learning Engineering and deploying production ML systems.
• Advanced Python programming and software engineering skills.
• Experience building real-time data streaming solutions for forecasting/logistics.
• Proven ability to design, deploy, and maintain scalable ML solutions in production.
• Experience with cloud platforms (AWS, Google Cloud) and microservices.
• Knowledge of Kubernetes and cloud-native architectures.

Responsibilities

• Design, develop, and deploy scalable ML models and applications.
• Research and implement advanced ML techniques for logistics and forecasting.
• Develop predictive models for delivery time estimation, forecasting, and pricing optimization.
• Build and maintain production-grade ML pipelines and infrastructure.
• Develop high-performance Python services and deploy on cloud platforms.
• Collaborate with Data Scientists, Software Engineers, and Product Managers.
• Monitor and improve model performance, scalability, and reliability.

Skills

Machine Learning Engineering

Python programming

AWS / GCP

Kubernetes

Microservices

Real-time data streaming

Production ML systems

Tools

Docker

Airflow

MLflow

Apache Flink

Job description

About the Role

An opportunity is available for an experienced Senior Machine Learning Engineer to develop, deploy, and optimise large-scale machine learning systems within a global technology and delivery platform.

The position focuses on building production-ready ML solutions that improve logistics operations, demand forecasting, delivery time predictions, pricing, and operational efficiency.

Working within a collaborative Data Science and Engineering environment, this role involves developing scalable machine learning applications, real-time data pipelines, and cloud-based systems that support millions of daily transactions.

Key Responsibilities

• Design, develop, and deploy scalable machine learning models and applications.
• Research and implement advanced ML techniques to solve complex logistics and forecasting challenges.
• Develop predictive models for delivery time estimation, supply and demand forecasting, and pricing optimisation.
• Build and maintain production-grade machine learning pipelines and infrastructure.
• Develop high-performance Python applications and services.
• Deploy and manage machine learning solutions using cloud platforms such as AWS or Google Cloud Platform.
• Work with real-time data streaming technologies and event-driven architectures.
• Develop and optimise microservices and cloud-native applications.
• Implement tools, data pipelines, and evaluation frameworks to support ML experimentation and deployment.
• Monitor and improve model performance, scalability, and reliability.
• Collaborate with Data Scientists, Software Engineers, Product Managers, and other technical stakeholders.
• Contribute to engineering best practices, technical design, and continuous improvement initiatives.

Required Skills & Experience

• Strong professional experience in Machine Learning Engineering, including developing and deploying production ML systems.
• Advanced Python programming and software engineering skills.
• Experience developing real-time data streaming solutions for forecasting, logistics, or similar applications.
• Proven ability to design, deploy, and maintain scalable ML solutions in production environments.
• Experience with AWS, Google Cloud Platform, or comparable cloud technologies.
• Strong understanding of microservices, event-driven architecture, serverless computing, and cloud architecture patterns.
• Experience deploying applications using Kubernetes.
• Experience working with large-scale, real-time production systems handling millions of requests or transactions.
• Strong problem-solving skills and the ability to work independently on complex technical challenges.
• Experience collaborating with cross-functional engineering and product teams.

Preferred Skills & Experience

• Experience with Docker, Airflow, MLflow, or Apache Flink.
• Knowledge of MLOps practices and automated machine learning deployment pipelines.
• Experience with logistics optimisation, demand forecasting, or dynamic pricing.
• Experience developing high-throughput, low-latency ML applications.
• Background in e-commerce, online marketplaces, delivery platforms, or logistics technology.

Working Arrangements

This is a hybrid position based in Berlin, Germany, requiring:

• 3 days per week working from the Berlin office.
• 2 days per week working remotely.

Additional Information

This position is suited to experienced machine learning engineers with strong software engineering capabilities and a proven track record of deploying scalable, real-time ML systems.

Hands-on experience with Python, cloud infrastructure, Kubernetes, and production machine learning applications is particularly important.