Senior Machine Learning Engineer

• You have built and shipped ML systems used by real users.

• You understand how modern ML models behave - and misbehave - in production.

• You write strong, production-quality code and think in systems, not scripts.

• You take ownership, work independently, and push work across the finish line.

• You learn fast, communicate clearly, and improve through iteration.

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.

• ML models and systems in production consistently meet accuracy, latency, reliability, and efficiency targets.

• Complex production issues are monitored, debugged, and resolved with minimal disruption.

• Training, inference, and data pipelines are robust, scalable, and maintainable over time.

• Drives measurable improvements in ML systems based on real-world signals and user feedback.

• Provides mentorship and technical guidance to peers, raising the overall ML engineering standard.

• Collaborates cross-functionally to ensure ML features integrate seamlessly into products and meet business goals.

As a Senior Member of Technical Staff, Machine Learning, you are an independent owner of critical ML subsystems in production. You take ambiguous problems, design practical solutions, and ship systems that operate reliably at scale.

This is a hands-on, high-impact role focused on depth.

• Build core ML systems that power a proactive, long-horizon AI product.

• Own work end-to-end: data preparation, training, evaluation, inference, and iteration.

• Turn research ideas into working systems that run reliably in production.

• Debug model failures and system issues using real production signals.

• Iterate quickly: ship, measure outcomes, refine, and repeat.

• Collaborate closely with research, product, and engineering to deliver real user impact.

• Mentor and review work from other ML engineers through example and technical judgment.

• Work under real production constraints: latency, cost, reliability, and safety

• Python

• PyTorch / JAX

• GPU-based training and inference systems