ML Engineer - End-to-End AI for Logistics

Everoad by sennder; Amsterdam; Sur place; EUR 90 000 - 130 000; Plein temps

Résumé du poste

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.

Responsabilités

• 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.

Connaissances

Data Science

ML Engineering

Statistical analysis

Hypothesis testing

Data exploration

Collaborative skills

Formation

Bachelor's degree in a relevant field

Outils

Python

SQL

Git

Snowflake

Jupyter Notebooks

Streamlit

PowerBI

Description du poste

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.