Machine Learning Engineer

• Design, implement, and train reinforcement learning and deep learning models for real-world optimization problems.
• Work with constraint-based problems and combinatorial optimization techniques.
• Translate research ideas and academic approaches into practical solutions.
• Work with large and complex datasets to identify patterns, develop models, and improve optimization outcomes.
• Collaborate with engineers and domain experts to integrate AI and optimization approaches into production systems.
• Write clean, maintainable Python code and contribute to testing, documentation, code reviews, and development workflows.
• Continuously evaluate and improve the performance, scalability, and reliability of AI-based solutions.

• Education: A Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Robotics, or a related field.
• Python: Strong Python programming skills and experience building software beyond notebooks and prototypes.
• Machine Learning: A strong understanding of machine learning and deep learning concepts, with practical experience developing and training models.
• Reinforcement Learning: A solid understanding of reinforcement learning concepts, with practical experience applying reinforcement learning to optimization problems.
• Optimization: Practical experience with combinatorial optimization, operations research, or mathematical programming.
• Production ML: Proven experience building, deploying, and maintaining machine learning models in production environments.
• Frameworks: Hands-on experience with PyTorch, TensorFlow, or similar ML frameworks.
• Problem-Solving: Strong analytical and problem-solving skills, with the ability to break down complex problems and develop practical solutions.
• Research: The ability to understand, implement, and adapt research-based approaches to practical problems.
• Version Control: Working knowledge of Git and modern version-control workflows.
• Strong written and verbal communication skills in English., * Experience with logistics, supply chain, packing, routing, scheduling, or other operational problems.
• Experience with cloud platforms such as Azure or Google Cloud Platform.
• Experience with API development, integration, and working with production systems.
• Experience translating research ideas to implementation.
• Experience working with large-scale datasets or computationally intensive problems.

• Remote-first and results-driven: Primary working hours are CET-aligned to enable close collaboration with the team.
• Research meets production: You will have the opportunity to experiment with new approaches while also working on solutions deployed in real-world applications.
• Startup pace: Workload may vary by phase; we stay connected, communicate frequently, and solve problems together.

We are a Swiss startup leveraging AI and advanced optimization algorithms to transform logistics. Our solutions help Fortune 500 companies reduce logistics costs by more than 20% while lowering CO emissions through smarter packing and freight utilization.