Machine Learning Engineer

Albatross
25 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English

Job location

Tech stack

Artificial Intelligence
Amazon Web Services (AWS)
Azure
Python
Machine Learning
TensorFlow
Software Engineering
PyTorch
Deep Learning

Job description

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.

Requirements

Do you have a Bachelor's degree?, * 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.

Benefits & conditions

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

About the company

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.

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