Want to know if this job is worth applying to?
Zürich, Zürich, Schweiz/Suisse/Svizzera/Svizra
Onsite
There are over 5 billion users using basic applications today such email, notes, tasks, calendar and they're not AI-native. Our mission is to build proactive applications for anyone in the world, who are not used to complex prompting. We aim to bring intelligence to conversations, errands, organising and workflows, with minimal to no prompting.
Our first application is an agent-first mobile communication app. We believe building applications that users use daily for communication, coordination and collaboration needs will help integrate AI into their lives. The net effect is to eliminate misinformation, achieve low coordination costs, enable high quality tasks and collaboration at scale with minimal or little prompting.
Develop and improve ML models for classification, retrieval, ranking and personalisation
Build classifiers and ranking models, optimising accuracy, precision, latency and inference cost
Improve search relevance and context retrieval using embeddings, hybrid search and reranking
Develop personalisation models using user behaviour and product data
Build evaluation pipelines, datasets and metrics to measure model quality and detect regressions
Train, fine-tune and deploy models using production ML pipelines
Monitor model performance, debug production issues and improve models based on evaluation results and user feedback
3+ years of machine learning engineering experience, including deploying models to production
Strong Python programming skills and experience with PyTorch or JAX
Solid understanding of machine learning algorithms, model training and evaluation
Experience with classification, ranking, retrieval, recommendation or personalisation models
Experience working with embeddings, vector search or information retrieval systems
Experience building data pipelines, training workflows and model inference services
Ability to evaluate model performance using relevant metrics and experimentation
Understanding of trade-offs between model accuracy, latency, computational cost and reliability
Experience with transformer models, LLM fine-tuning or embedding models
Experience with hybrid search, reranking and retrieval evaluation
Experience with recommendation systems or personalisation in consumer applications
Experience with large-scale ML training or inference infrastructure