ML Engineer - End-to-End AI for Logistics

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

Job summary

Everoad by sennder in Amsterdam is seeking a Machine Learning Engineer to join our ML team. This applied role blends data science with engineering to deploy production-ready models, from pricing and forecasting to recommender systems.

You will work closely with product managers and end-users, owning end-to-end ML projects from R&D to production, with a strong emphasis on collaboration, scalability, and results in a fast-paced logistics environment.

Qualifications

• 5+ years of hands-on experience in DS or ML Engineering.
• Strong foundation in statistics, hypothesis testing, and data exploration.
• Experience deploying models to production and writing modular code.
• Proficient in Python, SQL, Git; familiar with Snowflake and notebooks.
• Experience building dashboards (Streamlit/PowerBI) for stakeholders.
• Understanding of LLMs and AI tools in production environments.

Responsibilities

• Develop and iterate on bid estimation algorithms for pricing.
• Build predictive models to forecast carrier behavior and capacity.
• Maintain and improve recommender systems for operational use.
• Explore AI-driven logistics solutions and scalable ML prototypes.
• Collaborate with Product Managers to translate problems into ML hypotheses.
• Leverage ML platform to deploy models and gather feedback.
• Deliver end-to-end ML projects from R&D to production release.

Skills

Data Science

ML Engineering

Statistical analysis

Hypothesis testing

Data exploration

Collaborative skills

Education

Bachelor's degree in a relevant field

Tools

Python

SQL

Git

Snowflake

Jupyter Notebooks

Streamlit

PowerBI

Job description

Everoad by sennder in Amsterdam is seeking a Machine Learning Engineer to join our ML team. This applied role blends data science with engineering to deploy production-ready models, from pricing and forecasting to recommender systems.

You will work closely with product managers and end-users, owning end-to-end ML projects from R&D to production, with a strong emphasis on collaboration, scalability, and results in a fast-paced logistics environment.