Lead Engineer, Machine Learning

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 product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. We believe products will greatly reduce hallucinations

Our objective is to organise anyone's life, allowing us all to spend time on valuable and meaningful things

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.

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