Machine Learning Engineer
$72,000–$84,000 year
HybridAmsterdam, North Holland, The Netherlands
Job Summary
Own the full machine learning lifecycle, managing data pipelines, model training, evaluation, deployment, and monitoring in Azure cloud infrastructure. Collaborate with product, backend, and mobile engineers to operationalize features ranging from speech recognition and diarization to LLM-based note-taking and chat-based assistance. Transform early-stage prototypes into reliable production services while debugging infrastructure, improving annotation workflows, and exploring observability frameworks. This role thrives in a small, ambitious team focused on solving real user problems in healthcare environments. You will work on the Attendi App, a one-stop solution for AI-driven administration, helping accelerate the roadmap toward a comprehensive product experience for caregivers.
Required Qualifications
- 3+ years of industry experience in machine learning engineering or applied ML roles
- hands-on experience deploying, scaling, and operating ML systems in production
- comfortable with cloud-native infrastructure
- fluent in Python
- strong engineering fundamentals, including testing, monitoring, and performance considerations
- pragmatic, product-focused, and motivated by solving real user problems in complex environments like healthcare
Desired Qualifications
- Azure
- AWS/GCP experience
- experience with containerizing ML inference and/or training
- experience in one or more of the following: Speech technologies (ASR, VAD, diarization)
- experience in one or more of the following: Evaluating, training or finetuning LLMs
- experience in one or more of the following: ML platform-level frameworks, such as Ray, Dagster, Flyte, MLFlow, CometML
- experience in one or more of the following: Data engineering
- Understand the unique constraints of healthcare environments: security, privacy, and safe clinical decision support
- experience with web app backends in Python and management of databases
- experience with annotation, ML observability, or other platform level frameworks for ML
- familiar with RAG systems, vector databases, or clinical knowledge-grounding
- Enjoy improving engineering culture and shaping technical direction in a growing startup
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