Machine Learning Engineer, Ranking & Retrieval

About

There are over 5 billion users using basic applications today such email, notes, tasks, calendar and theyre 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 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.
Python
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.
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.
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.
Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.
We make decisions collectively and move quickly, balancing high-quality work with continuous learning.
Joining our team requires the ability to bring structure, exercise judgment and execute independently. Applications are evaluated by our technical team members.