Machine Learning Engineer II

Machine Learning Engineer II

Location:

sennder HQ Berlin | Machine Learning | Hybrid | Full-time

sennder is Europe's Leading Digital Freight Forwarder. In a traditional industry, we're moving fast and focusing on the digitalization and automation of road logistics.

By leveraging our proprietary technology, we're building an ecosystem that is leading the industry into the 21st century.

We are a growing team looking for a ML Engineer II in the Data/ML department to help us on our journey to revolutionize road freight logistics. The role is located in our Berlin, Barcelona or Amsterdam offices.

We are seeking a Machine Learning Engineer II to join our ML team. You will work on a diverse portfolio of high-impact projects, ranging from optimizing our established Load-to-Carrier Recommendation engine to building greenfield GenAI solutions that automate complex Operator workflows.

You will act as an end-to-end ML Engineer, taking ownership from "Light MVP" prototyping to production deployment. You will tackle challenges involving both structured data (ranking/matching) and unstructured data (text/NLP).

• Develop and deploy NLP & LLM solutions: Build production-grade systems for email classification, entity extraction, and automated bidding strategies using modern transformer-based architectures and LLM APIs.
• Maintain and refine Recommendation Systems: Optimize existing recommender pipelines to ensure carriers are matched with the best loads.
• Engineer scalable ML pipelines: Design robust training and inference pipelines, ensuring your models are reliable, observable, and meet strict SLAs.
• Handle unstructured data at scale: Work with Data Engineering to turn messy logistics data (emails, PDFs, chat logs) into structured features.
• Drive the product strategy: Work closely with Product Managers and Operators to drive the adoption of ML-powered products and revolutionize the logistic industry.
• Drive experimentation:
  Implement A/B tests and offline evaluation frameworks to measure the real-world business impact of your models, favoring pragmatic solutions over theoretical complexity.

• Experience: 5+ years of experience as a Machine Learning Engineer or Data Scientist with a strong software engineering component.
• Applied ML Expertise: Solid grasp of NLP (Transformers, LLMs, Hugging Face) and Recommender Systems (collaborative filtering, learning-to-rank, two-towers architecture). You know when to use a simple regression and when to deploy a complex deep learning model.
• Software Engineering proficiency: Strong fluency in Python (incl. Scikit learn, pandas and backend designs), you write clean, modular, and testable code. Experience with API frameworks (FastAPI, Flask), containerization (Docker, Kubernetes) and ML frameworks (Tensorflow, PyTorch) is required. You are familiar with AWS, Terraform and datadog.
• Production Mindset: Experience deploying models to production environments. Familiarity with model serving tools (e.g.,
  Bento
  
  ML, MLFlow
  ) and workflow orchestration (e.g.,
  Flyte
  , Airflow) is a strong plus.
• Data Fluency: Advanced SQL skills and experience working with cloud data warehouses (e.g.,
  Snowflake
  ).
• Problem Solver: You are motivated by solving business problems, not just technical ones. You are comfortable navigating ambiguity and iterating fast to validate hypotheses.
• Excellent communication skills
  : with a comfort level in explaining technical trade-offs to diverse audiences ranging from developers to stakeholders.

• Familiarity with MLOps tools and practices, such as containerization (Docker, Kubernetes), model versioning systems, and infrastructure automation (e.g., Terraform).
• A background in advanced data engineering concepts such as real-time streaming, large-scale ETL workflows, or AI-specific data pipelines.
• Experience deploying models at scale and creating fault-tolerant architectures.
• Experience with reinforcement learning applications in pricing or revenue optimization, and knowledge of game theory, auction mechanisms, or marketplace dynamics, to collaborate with our 2nd ML team.

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