Location
Remote in Europe.
Albatross
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.
The Role
As a Data Scientist, you will design and deploy machine learning models that power real-time personalization for our customers. You will own defined workstreams of ML projects end-to-end, and you will work closely with Applied Scientists and Engineers to translate product and customer needs into scalable ML solutions. More specifically, you will:
- Design and implement machine learning models for ranking, recommendation, and personalization.
- Define feature engineering pipelines and modeling strategies for customer use cases.
- Train, evaluate, and deploy models using our internal ML tooling and infrastructure.
- Own project workstreams from data preparation through production deployment.
- Collaborate with Applied Scientists to integrate new algorithms into production systems.
- Contribute improvements to internal ML tooling and experimentation infrastructure.
- Monitor model performance and iterate based on real-world feedback.
Requirements
- Bachelor's degree in Machine Learning or STEM.
- Strong background in machine learning, statistics, or data science.
- Solid programming skills in Python.
- Experience training and deploying ML models in production environments.
- Familiarity with ML frameworks such as PyTorch, TensorFlow, or JAX.
- Experience working with large-scale datasets and feature engineering pipelines.
- Ability to work independently on moderately complex ML problems.
- Strong communication skills in English.
Nice to Have
- Experience with recommender systems, ranking models, or search.
- Experience with large-scale experimentation and evaluation pipelines.
- Familiarity with learning-to-rank models, bandits, or reinforcement learning.
- Experience working with cloud environments such as AWS, GCP, or Azure.
Benefits
- Flexibility to work from anywhere across Europe.
- Budget for learning and training, attend events and conferences.
