Technical Lead, Machine Learning

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.

• 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

• 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

• 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