About

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.

Role

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

Requirements

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

Nice to Have

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