ML Engineer — End-to-End AI for Smart Logistics

sennder; Amsterdam; On-site; EUR 90,000 - 130,000; Full time

Job summary

sennder, Europe’s leading digital freight forwarder, is looking for a Machine Learning Engineer to join its ML team in Amsterdam. You will work on applied ML projects, from pricing and recommender systems to greenfield solutions, collaborating with Product Managers, end-users and Platform Engineers to turn workflows into measurable ML outcomes.

The role blends data science exploration with production ML engineering, requiring end-to-end ownership from R&D to deployment in a fast-paced, hybrid

Qualifications

• 5+ years of hands-on experience in Data Science orMachine Learning Engineering.
• Strong foundation in statistical analysis and data exploration to validate assumptions.
• Experience deploying models to production and writing modular code.
• Proficiency in Python, SQL, and Git; familiarity with cloud data warehouses.

Responsibilities

• Pricing Engine Optimization: develop bid estimation algorithms and margin-optimization models.
• Carrier Forecasting: build models to forecast carrier behavior and capacity.
• Recommender Systems: maintain and improve recommender systems.
• Industry Innovation & Applied ML: explore ML for logistics challenges and data-driven solutions.
• Product Discovery & Ideation: translate operational pain points into ML hypotheses and prototypes.
• AI & LLM Integration: evaluate and leverage LLMs and AI tools in production.
• Platform Collaboration: work with Data & AI Platform to deploy models and provide feedback.
• End-to-End Execution: oversee R&D to production deployment and monitoring.

Skills

Data Science fundamentals

ML Engineering

Python

SQL

Git

LLMs

Communication

Collaboration

Tools

Snowflake

Jupyter Notebooks

Streamlit

PowerBI

Job description

sennder, Europe’s leading digital freight forwarder, is looking for a Machine Learning Engineer to join its ML team in Amsterdam. You will work on applied ML projects, from pricing and recommender systems to greenfield solutions, collaborating with Product Managers, end-users and Platform Engineers to turn workflows into measurable ML outcomes.

The role blends data science exploration with production ML engineering, requiring end-to-end ownership from R&D to deployment in a fast-paced, hybrid