Machine Learning Engineer

What you'll do

This year, we're ramping up our machine learning efforts, focusing particularly on leveraging open source large language models to enhance our platform. Our MLEs play a critical role in shaping these initiatives from the ground up - from conceptualizing new ideas to bringing them to life through development, deployment, and ongoing optimization.

We're using on-premise hardware for model prototyping and development. Plus, our growing MLops infrastructure supports our team in experiment management, ensuring efficient tracking, smooth deployment, and meticulous monitoring of our models.

Key Responsibilities:

• Apply Machine Learning / Artificial Intelligence to personalization, discovery, fraud detection and natural language processing;

• Design Machine Learning solutions for TicketSwap’s unique product challenges;

• Manage models entire lifecycle from prototyping, to deployment, to continuous improvement and monitoring;

• Producing data insights: extract value from data and drive improvements and strategic decisions;

• Work closely with the different stakeholders, understand their problems and come up with solutions;

• Help with the continuing development of our MLops pipeline;

• With data driven decision making playing an important role the ML products you develop will have real impact on customers and the business.

About you

• Work experience as a Machine Learning Engineer or a Data Scientist;

• You are based in The Netherlands, are an EU resident, or have an existing visa to work here. A relocation package is not provided;

• Solid theoretical knowledge of Machine Learning;

• Familiarity with at least 2 of: Scikit Learn, TensorFlow/Keras, PyTorch, one of the llama or LLM ecosystems;

• Strong understanding of Python and OOP design principles;

• Good knowledge of data preprocessing and feature engineering;

• Comfortable with SQL;

• Ability to work with git, write code reviews, and tests;

• Passionate about building delightful products for users;

• Strong communication and team-work skills.

Nice to have

• Working experience with ETL and Data Pipelines;

• Familiarity with tools relevant to our workflow: AWS (S3, ec2, Redshift, SageMaker), ClearML, Hydra Framework, Docker;

• Experience with MLops beyond experiment tracking;

• Knowledge of statistics/statistical modeling.