Machine Learning Engineer, Applied AI

• Python

• PyTorch / JAX

• Embeddings, vector search and ranking models

• Production ML systems and inference pipelines

• Strong foundations in machine learning and modern neural architectures.

• Hands-on experience building and deploying ML models in production.

• Experience in classification, ranking, retrieval, recommendation systems or personalization.

• Understanding of model evaluation, experimentation and performance metrics.

• Comfortable writing production-quality code and working with real-world datasets.

• Able to balance model quality, latency, cost and reliability.

• Strong ownership, problem-solving skills

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.

Build 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.

• Build and improve ML models for classification, ranking, retrieval and personalization.

• Develop efficient classifiers and optimize model accuracy, precision, latency and cost.

• Improve search relevance and context retrieval using embeddings, hybrid search and reranking.

• Build personalization models that learn from user behavior while respecting privacy.

• Develop evaluation pipelines, labeled datasets and metrics to measure model quality.

• 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.

• ML models in production meet expected accuracy, relevance, latency and reliability targets.

• Classification and ranking improvements deliver measurable gains in model quality.

• Evaluation pipelines identify regressions and support reliable model releases.

• Training, inference and data pipelines are robust, reproducible and maintainable.

• Model improvements are driven by real-world feedback and measurable results.

• Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.