[Hiring] Applied Scientist @Albatross

Role Description

As an Applied Scientist you will help push the boundaries of real-time machine learning, personalization and search. You will design state-of-the-art algorithms and turn them into product-ready capabilities that power decision-making across billions of interactions. More specifically you will:

• Design, implement, and improve the ML algorithms that power our recommendation and search engine.
• Prototype and validate novel approaches in areas such as deep learning, reinforcement learning and LLMs.
• Collaborate with Data Scientists and Engineers to generalize solutions and contribute to shared tooling.
• Maintain a high standard of code quality and experimentation discipline.
• Publish technical work and help represent Albatross in the ML research community and ensure our approach remains ahead of the curve.
• Work backward from product goals to guide the development of algorithmic solutions.
• Drive innovation in areas such as transformer architectures, multimodal embeddings, advanced information retrieval, and scalable retraining.

Qualifications

• PhD in Machine Learning, Computer Science, Mathematics, or a related field.
• Proven experience in developing and shipping ML models in production.
• Strong Python skills and experience with ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience reading and implementing ideas from academic papers.
• Understanding of algorithms for search, ranking, recommendation, or representation learning is a plus.
• Publications in top ML or data science conferences (e.g. NeurIPS, ICML, KDD, RecSys, SIGIR) are a plus.
• Strong communication skills in English and collaboration skills.
• Flexibility to work from anywhere across Europe.

Benefits

• Budget for learning and training, attend events and conferences.

Company Description

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.