Machine Learning Engineer II
Sennder
Job Overview
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Job Description
About sennder
sennder is Europe's Leading Digital Freight Forwarder. In a traditional industry, we are moving fast by digitalizing and automating road logistics. Join our growing team as a Machine Learning Engineer II in our Data/ML department at our Berlin, Barcelona or Amsterdam offices.
What You Will Do
As a Machine Learning Engineer II, you will work on a diverse portfolio of high-impact projects. Your responsibilities include:
- Develop and deploy NLP and LLM solutions for email classification, entity extraction, and bidding strategies.
- Maintain and refine Recommendation Systems ensuring optimal matching between loads and carriers.
- Engineer scalable ML pipelines from prototyping to production deployment.
- Process unstructured data by collaborating with Data Engineering to create structured features.
- Drive product strategy and experimentation through A/B tests and offline evaluations.
What We Are Looking For
You should have 5+ years of experience, a strong background in NLP (Transformers, LLMs, HuggingFace) and recommender systems, and proficient software engineering skills in Python. Experience with AWS, API frameworks, containerization, and ML frameworks is required. A production mindset with advanced SQL skills and a passion for solving practical business problems is essential.
Nice to Have
Additional experience with MLOps tools, real-time streaming, and reinforcement learning applications will be a plus.
Why Choose sennder
sennder offers a dynamic scale-up environment with face-to-face connections, innovative workspaces, a focus on employee well-being, and growth opportunities with competitive rewards.
Key skills/competency
- NLP
- LLM
- Recommendation Systems
- Python
- ML Pipelines
- Data Engineering
- API Frameworks
- Containerization
- AWS
- SQL
How to Get Hired at Sennder
- Research sennder's culture: Understand their digital freight innovation and values.
- Tailor your resume: Highlight ML engineering, Python, and AWS skills.
- Showcase projects: Detail NLP and recommender system implementations.
- Prepare for technical interviews: Practice ML pipeline and deployment discussions.
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