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# Medior Machine Learning Engineer

- **Company:** Attendi
- **Location:** Amsterdam, Netherlands
- **Experience:** Experienced
- **Salary:** €7,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon Web Services, Microsoft Azure, Databases, Information Engineering, Software Debugging, Python (Programming Language), Machine Learning, Azure Machine Learning, Web Applications, Cloud Platform System, Large Language Models, Backend, Machine Learning Operations
- **Published:** August 7, 2026
- **Apply:** https://nl.indeed.com/viewjob?jk=5d471b18759ad9b5

## About the Role

You might be a good fit if you hit most or all of the following:

 * Have 3+ years of industry experience in machine learning engineering or applied ML roles.
 * Have hands-on experience deploying, scaling, and operating ML systems in production.
 * Are comfortable with cloud-native infrastructure (Azure preferred, but AWS/GCP experience is also valuable).
 * Are fluent in Python and have strong engineering fundamentals, including testing, monitoring, and performance considerations.
 * Enjoy collaborating cross-functionally and contributing beyond your specialty - this is a small team where ownership is encouraged.
 * Are pragmatic, product-focused, and motivated by solving real user problems in complex environments like healthcare., * Have experience with containerizing ML inference and/or training.
 * Have experience in one or more of the following:
 + Speech technologies (ASR, VAD, diarization)
 + Evaluating, training or finetuning LLMs
 + ML platform-level frameworks, such as Ray, Dagster, Flyte, MLFlow, CometML
 + Data engineering
 * Understand the unique constraints of healthcare environments: security, privacy, and safe clinical decision support.
 * Have experience with web app backends in Python and management of databases.
 * Have experience with annotation, ML observability, or other platform level frameworks for ML.
 * Be familiar with RAG systems, vector databases, or clinical knowledge-grounding.
 * Enjoy improving engineering culture and shaping technical direction in a growing startup.

## Description

* Own multiple parts of the machine learning lifecycle: data pipelines, model training, evaluation, deployment, monitoring, and iteration.
 * Work on scalable ML systems in the cloud (Azure).
 * Work closely with product, backend, and mobile engineers to bring ML-powered features -from transcription to note taking to chat-based agentic assistance - into the hands of healthcare professionals.
 * Participate in early-stage exploration of new ML features and transform promising prototypes into reliable production services.
 * Wear multiple hats in a pragmatic way: from exploring LLM observability frameworks to debugging infrastructure to improving annotation workflows-whatever moves the product forward.


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