Lead Engineer, Machine Learning

About the role

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.

As Lead Engineer, Machine Learning, you own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems.

You will work across the model lifecycle: data, training, evaluation, inference, and deployment. This is a hands-on leadership role for someone who wants to operate at the intersection of research, systems, and product.

WHAT YOU'LL OWN

- Own the end-to-end ML systems powering our company, from data and training to evaluation, inference, and deployment.

- Build and evolve training and fine-tuning pipelines for large models.

- Design evaluation systems that measure capability, robustness, safety, and real-world product performance.

- Architect high-performance inference systems, optimizing latency, GPU utilization, memory, cost, and reliability.

- Build data pipelines and systems for high-quality real-world and synthetic training data.

- Establish reliable production infrastructure for deploying, monitoring, and continuously improving models.

- Partner closely with research and application engineering to turn model capabilities into product improvements.

- Make pragmatic technical trade-offs and rapidly iterate based on real-world performance.

WHAT WE'RE LOOKING FOR

- Experience building and shipping ML systems used in production, not just research prototypes.

- Strong understanding of modern large-model training, fine-tuning, evaluation, and inference.

- Strong software engineering and systems fundamentals.

- Experience operating ML workloads at meaningful scale, particularly GPU-based systems.

- Strong technical judgment and the ability to navigate ambiguous problems independently.

- A bias toward experimentation, measurement, and shipping.

- High standards for correctness, reliability, and production quality.

OUTCOMES

- Research and models reliably translate into production-ready solutions with clear performance and quality targets.

- ML pipelines, training loops, and inference systems are stable, efficient, and maintainable.