Location: Remote in Europe
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 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.
Requirements
- 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.
Benefits
- Flexibility to work from anywhere across Europe.
- Budget for learning and training, attend events and conferences.