Company OverviewAt 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.
LocationRemote in Europe.
RoleAs 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
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