At Mirakl, the global leader in marketplace platform software, the AI Engineer plays a pivotal role in scaling the technology that powers hundreds of e-commerce platforms worldwide. As SaaS marketplaces grow, they ingest millions of product data points from thousands of third-party sellers. The AI Engineer is responsible for building, deploying, and optimizing the machine learning models and pipelines that make sense of this massive, unstructured data stream.

Your work will directly impact core platform capabilities, such as automated product categorization, catalog matching, semantic search, and seller onboarding. By leveraging advanced natural language processing, large language models, and traditional machine learning, you will transform chaotic seller data into structured, high-quality catalogs. This ensures a seamless shopping experience for millions of end-users and drives immediate business value for Mirakl's enterprise clients.

This role sits at the intersection of software engineering and data science. You will not just train models in isolation; you will design production-grade systems capable of handling high-throughput, low-latency inference. Joining this team means tackling complex scalability challenges alongside a highly collaborative group of engineers who value clean code, pragmatic architecture, and continuous innovation.

To succeed in the Mirakl selection process, you must be prepared for a mix of deep technical inquiries, practical coding evaluations, and behavioral assessments. The questions below represent patterns observed in real interview loops for the AI Engineer position. They are designed to test your core engineering foundations, machine learning intuition, and ability to deliver production-ready code.

Python & Software Engineering Foundations

These questions assess your mastery of Python, focusing on language internals, performance optimization, and clean coding practices.

• Explain how memory management works in Python, and how you would identify a memory leak in a long-running data pipeline.
• What are the differences between a list comprehension, a generator expression, and a standard loop in terms of memory and execution speed?

Preparing for the Mirakl interview loop requires a balanced strategy. You cannot rely solely on theoretical machine learning knowledge or pure algorithmic competitive programming. The hiring team looks for well-rounded engineers who write clean, maintainable code and understand how to build systems that deliver business value.

To stand out, you should focus your preparation on four key evaluation criteria:

Technical Mastery – Demonstrate a deep understanding of Python internals, data structures, and machine learning fundamentals. You should be comfortable explaining not just how to use a framework like PyTorch or Hugging Face, but also the underlying mechanics of the algorithms you employ.

Pragmatic Problem-Solving – Mirakl values engineers who choose the right tool for the job. Avoid over-engineering; show that you can balance model complexity with execution cost, latency, and maintainability. A simple heuristic that works is often preferred over a complex model that is impossible to deploy.

System Architecture & Scalability – Be ready to discuss how models operate at scale. Think about data pipelines, database choices, caching mechanisms, and how to serve models efficiently to handle thousands of requests per second.

Communication & Collaboration – You must be able to articulate your technical decisions clearly. Whether you are explaining your take-home project or walking through a behavioral scenario, focus on structuring your thoughts logically and explaining the "why" behind your actions.

The interview process at Mirakl is thorough, structured, and designed to evaluate both your immediate technical capabilities and your long-term potential within the engineering organization. Candidates frequently note that the process moves at a steady, efficient pace, with clear communication from the recruitment team at every stage.

The evaluation is rigorous, and the standards are high—even for intermediate roles, the team expects strong software engineering foundations. The journey begins with an initial screening to align on expectations, followed by a practical take-home assignment that serves as the centerpiece of the technical evaluation. Subsequent rounds dive deep into your code, your machine learning expertise, and your cultural alignment with the team.

The timeline above outlines the standard progression for the AI Engineer pipeline. This structured sequence ensures that both you and the hiring team have multiple opportunities to assess mutual fit before an offer is extended. While the exact duration can vary depending on candidate availability, the entire loop is typically completed within three to four weeks.

To pass the technical bar at Mirakl, you must perform exceptionally well across several distinct evaluation areas. Understanding what is expected in each phase will help you direct your preparation effectively.

Python & Software Engineering Foundations

This area evaluates your ability to write clean, production-grade Python code. Mirakl builds enterprise software, meaning your code must be robust, readable, and highly optimized. Interviewers will assess your familiarity with modern Python features, testing frameworks, and performance profiling.

Be ready to go over:

• Object-Oriented Programming (OOP) in Python – Structuring classes, inheritance, and utilizing design patterns effectively.
• Code Modularity and Readability – Writing clean functions, proper type hinting, and adhering to PEP 8 standards.
• Testing and CI/CD – Writing comprehensive unit and integration tests using frameworks like pytest.

Advanced concepts (less common):

• Asynchronous programming (asyncio) for handling I/O-bound tasks.
• Memory management, garbage collection, and profiling memory usage in data-heavy applications.

Example questions or scenarios:

• "Refactor this monolithic script into a clean, testable, and modular Python package."
• "How would you optimize a custom data loader that is currently bottlenecking your GPU utilization during training?"

Machine Learning Engineering & NLP

This evaluation area focuses on your ability to build, train, and evaluate machine learning models, with a strong emphasis on Natural Language Processing (NLP). Because Mirakl deals with vast amounts of textual product data, your understanding of text preprocessing, representation, and classification is critical.

Be ready to go over:

• Text Preprocessing & Tokenization – Handling noisy, multilingual text data from global sellers.
• Feature Engineering & Embeddings – Creating and utilizing dense vector representations for semantic tasks.
• Model Evaluation Metrics – Choosing and interpreting metrics like Precision, Recall, F1-Score, and ROC-AUC, especially under class imbalance.

Advanced concepts (less common):

• Vector database integration (e.g., Pinecone, Milvus) for efficient similarity search.
• Parameter-efficient fine-tuning (PEFT) techniques like LoRA for large language models.

Example questions or scenarios:

• "Design a model to predict the correct category of a product based solely on its title and a short, unstructured description."
• "How would you build a validation pipeline to ensure that a newly trained catalog-matching model does not introduce regressions for existing categories?"

The Take-Home Case Study & Restitution

The take-home project is a crucial component of the Mirakl interview process. You will be given a real-world problem (typically a machine learning or data processing challenge) and a week to complete it. The expectation is a working, clean, and well-documented solution that takes roughly 2 to 4 hours of active development.