Machine Learning Engineer (II and Senior)

Requirements:

• You have 5 + years of experience as an engineer working in the machine learning domain
• 5 years of experience with one or more general purpose programming languages including but not limited to: Java, C/C++ or Python
• 5 years of experience in software development
• 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture
• 5 years of experience building and deploying Machine Learning systems in (prediction, ranking, embedding, deep learning) in production and experience building architecture in different modeling domains
• 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning)
• You have experience leveraging big data to create the pipelines needed to feed the models with appropriate data
• You have a strong understanding of good software engineering practices as well as data engineering and MLOps principles
• You have strong familiarity with the standard data science toolkit in python, such as (py)spark, (Trino) SQL, Tensorflow, PyTorch, XGBoost/LightGBM, Pandas, MLFlow or similar MLOps frameworks, and Airflow
• You have knowledge/experience of working with ML infrastructure components with tools such as k8s, docker, airflow, argo-workflows, prometheus, grafana
• You have an experimental mindset with a launch fast and iterate mentality
• You proactively take the lead in projects, from ideation to deployment. You have experience working with a wide range of stakeholders and can clearly communicate complex outcomes over a wide range of audiences

Nice to Have:

• You have experience with distributed GPU compute environments
• You have experience working with a Machine Learning ‘Feature Store'

What You'll Be Doing:

• Develop and maintain production ML pipelines for data ingestion, training, validation, and deployment. Examples ML domains are: on-line learning algorithms to pick the best optimization decision in a changing environment, clustering algorithms to group customers/shoppers, supervised and semi-supervised learning methods for inference on risk patterns or graph analysis, representation learning for behavior prediction and monitoring, Anti-Money Laundering (AML) systems and real-time anomaly detection based on time-series modeling
• Identify and fix performance bottlenecks in ML training and inference (memory consumption, online latency, training time etc.)
• Collaborate with software engineers to integrate ML solutions into products and services
• Collaborate with data scientists to transition research prototypes into scalable solutions
• Collaborate with MLOps and platform teams to integrate effectively with current tools, and shape priority for future tools
• Support and encourage good engineering practices on product ML teams

Perks and Benefits:

• Full-time position based in our Amsterdam office
• Relocation required if currently living outside of the Netherlands