Machine Learning Engineer

As a Machine Learning Engineer, you'll scale our personalization systems, optimizing large-scale training pipelines, building high-performance vector stores, and enabling scientists to bring advanced AI models to production. More specifically you will:

• Develop and optimize distributed training pipelines for large-scale deep learning models.
• Build and maintain high-performance vector storage systems.
• Drive hyperparameter optimization workflows to accelerate experimentation and improve model performance.
• Collaborate with Applied Scientists to translate research prototypes into production-ready pipelines.
• Write clean, efficient, and maintainable code with a focus on performance and scalability.

• Strong software engineering background, with fluency in Python and/or Rust.
• Experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Good understanding of machine learning concepts and workflows.
• Familiarity with cloud platforms (AWS, GCP, or Azure).
• Ability to collaborate closely with scientists and translate research needs into engineering solutions.
• Curiosity, adaptability, and eagerness to learn new tools and techniques.
• Strong communication skills in English.

• Flexibility to work from anywhere across Europe.
• Budget for learning and training, attend events and conferences.

At Albatross, we're building the second pillar of AI: a perception layer that understands how users actually experience content, in real time. Trained on live user interactions, Albatross learns and reasons on the fly. Our technology powers real-time, in-session discovery by adapting to evolving user interests, in real-time. We have raised significant funding and our platform already operates at scale, with billions of events being processed and hundreds of millions of predictions served.