About the Role

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

Design and build ML systems covering data processing, model training, evaluation, inference and deployment

Develop training and fine-tuning pipelines for large language models

Build evaluation frameworks to measure model quality, reliability and performance

Optimise inference systems for latency, throughput, GPU utilisation and cost

Develop data pipelines for collecting, processing and validating training and evaluation datasets

Deploy, monitor and troubleshoot ML models and infrastructure in production

Make architecture decisions and guide engineers through technical design and code reviews

Work with research and application engineering teams to improve model performance in production

Requirements

8+ years of software engineering or machine learning engineering experience, including production ML systems

Strong Python programming skills and hands-on experience with PyTorch or JAX

Experience training, fine-tuning and evaluating large machine learning models

Experience building and operating GPU-based training or inference systems

Strong understanding of distributed computing, data pipelines and ML infrastructure

Experience optimising model performance, inference latency and resource utilisation

Ability to design ML system architecture, review code and resolve complex production issues

Experience providing technical leadership to ML engineers

Nice to Have

Experience with large language models, transformers and distributed model training

Experience with inference frameworks such as vLLM or TensorRT-LLM

Experience with synthetic data generation, data curation and model evaluation pipelines

Experience deploying ML systems for consumer applications