AboutThere 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.
RoleBuild and improve ML systems that power our AI-native applications.
Work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment.
This role is for engineers who enjoy solving practical ML problems, improving model quality and shipping reliable systems used by real users.
FocusBuild and improve ML models for classification, ranking, retrieval and personalization.
Improve search relevance and context retrieval using embeddings, hybrid search and reranking.
Build personalization models that learn from user behavior while respecting privacy.
Fine-tune and adapt existing models when supported by sufficient data.
Build and maintain ML training, inference and deployment pipelines.
Debug production issues, monitor model performance and ship improvements based on real-world feedback.
Tech StackPythonPyTorch / JAXEmbeddings, vector search and ranking modelsProduction ML systems and inference pipelines Ideal ExperienceStrong foundations in machine learning and modern neural architectures.
Hands-on experience bui.