Tech Lead, Machine Learning

Help build AI for the 5 billion people who don't want to write prompts.

Billions of people use email, notes, tasks and calendars every day, and none of those tools are AI-native. We're building proactive applications that bring intelligence to conversations, errands, organising and workflows, with little to no prompting. Our focus is reliability: long-running workflows, persistent context, and tasks that actually get completed in the real world, with far fewer hallucinations. The goal is simple.

Organise people's lives so they can spend their time on what matters.

The role

You'll own the execution layer of our intelligence, turning research and model capabilities into reliable, scalable production systems. It's a hands-on leadership role at the intersection of research, systems and product, covering the full model lifecycle: data, training, evaluation, inference and deployment.

What you'll own

• End-to-end ML systems, from data and training through to evaluation, inference and deployment
• Training and fine-tuning pipelines for large models
• Evaluation systems that measure capability, robustness, safety and real-world product performance
• High-performance inference: latency, GPU utilisation, memory, cost and reliability
• Data pipelines for high-quality real-world and synthetic training data
• Production infrastructure to deploy, monitor and continuously improve models
• Close partnership with research and application engineering, making pragmatic trade-offs and iterating quickly on real-world results

What we're looking for

• Experience building and shipping ML systems used in production, not just research prototypes
• A strong grasp of modern large-model training, fine-tuning, evaluation and inference, and of how large models fail
• Strong software engineering and systems fundamentals, with production-grade code and high standards for correctness and reliability
• Experience running ML workloads at meaningful scale, particularly on GPUs
• Sound technical judgment, and the ability to handle ambiguous problems independently
• A bias toward experimentation, measurement and shipping

Success looks like

• Research and models reliably become production-ready solutions with clear performance and quality targets
• Stable, efficient, maintainable pipelines, training loops and inference systems
• Production issues found and fixed quickly, with minimal user impact
• A supported, aligned team delivering high-impact work with little friction
• Measurable, safe iterations that improve the user experience over time

Tech stack: Python, PyTorch / JAX, GPU-based training and inference systems

Package: Negotiable Salary (depending on experience) + Equity + Benefits