ML / AI Engineer Intern (m/f/d)

How you can contribute

As a Machine Learning Engineer intern, you'll play a pivotal role in constructing, refining, and maintaining the infrastructure for training and deploying machine learning models. Your primary focus is to craft scalable, robust, and production-ready ML solutions. Collaborating closely with our software engineers, your journey will be guided by a personal mentor dedicated to your learning and growth. This collaborative effort aims to bring ML models to life in real-world applications, particularly within the dynamic context of industrial procurement and supply chains.

Your responsibilities

• Infrastructure Development: Assist in building and enhancing infrastructure for modern machine learning applications, focusing on modularity and scalability.
• Great User Experience: Collaborate with customers and product teams to improve user experience and contribute to result fine-tuning.
• Workflow Optimization: Support the design and optimization of APIs for streamlined machine learning workflows, integrating best practices.
• Deployment and Monitoring: Contribute to the deployment of ML models in production and assist in establishing monitoring procedures for stability.
• Shape the Stack: Stay informed about ML tools and concepts, actively participating in tool selection for tasks.
• Sharing Best Practices: Engage in knowledge sharing within the team to build a common understanding across the organization.

Let's talk about you

We’re looking for a talented ML engineer intern to help us build the vision of making supply chains digital, efficient, and sustainable for our customers. Now you may wonder what frameworks, tools, and languages you need to master. We believe that problem-solving, creativity, and drive are more important than tools that can be picked up. However, the following references will give a guideline of what experiences we think might be helpful.

• Demonstrated experience in creating and deploying machine learning applications in real-world scenarios.
• Strong programming abilities in languages like Python, and some familiarity with machine learning libraries such as TensorFlow, PyTorch, or scikit-learn.
• Exposure to setting up a production-ready machine learning stack that is modular and ready to scale.
• A solid understanding of software engineering best practices.
• Keeping up-to-date with the latest developments in the ML field, such as LLMs, LMMs, etc.
• Proficiency in optimizing and troubleshooting production code.
• Making informed trade-off decisions and understanding how to make sensible choices based on the current phase of the company.
• Excellent communication and collaboration skills, enabling effective teamwork with cross-functional teams.

Our Tech Stack

• Backend: Python, FastAPI, SQL Alchemy, Alembic, Pydantic, Pytest, …
• Frontend: TypeScript, NextJS, Zustand, Material UI, React Hook Form, …
• DB: PostgreSQL
• Data: Python, Prefect, Pandas, …
• Cloud: Azure
• Other: GitHub, GitHub Actions, Dependabot, Pre-commit, Mozilla SOPS, …

It is not expected to be completely familiar with each and every aspect of our tech stack.
Other technologies that we enjoy discussing and encourage in the team:

• Backend: Golang, Rust
• Frontend: Vue / Nuxt, Svelte / Svelte Kit, Tailwind, Bun, tRPC, …
• Linux: Nix, Arch, Ubuntu, i3, …
• Other: LLMs, LMMs, NeoVim Configs, …