Member of Technical Staff, Machine Learning

Responsibilities

• ● There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organising and workflows, with minimal prompting.
• ● Our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. Our objective is to help users complete tasks daily enjoyable with over 90% reduced time.
• ● As a Member of Technical Staff, Machine Learning, you will build core ML components. You will work on real production systems from day one, learning how large-scale ML behaves outside of research settings.
• ● This role is for engineers who want to develop strong systems judgment by shipping, debugging, and iterating on real-world ML.
• ● Build and improve ML components across data, training, evaluation, and inference.
• ● Fine-tune and adapt models as part of larger production systems.
• ● Implement evaluation and testing to understand model behavior.
• ● Help build and maintain data pipelines for real-world and synthetic data.
• ● Debug model issues, performance problems, and production incidents.
• ● Ship improvements iteratively and learn from real user feedback.
• ● Work closely with senior ML engineers and product teams.
• ● Work under real production constraints: latency, cost, reliability, and safety
• ● PyTorch / JAX
• ● Production ML systems running on GPUs
• ● IDEAL EXPERIENCE
• ● Strong foundations in machine learning and modern neural architectures.
• ● Some hands-on experience training, fine-tuning, or deploying ML models.
• ● Comfortable writing production-quality code and learning new tools quickly.
• ● Curious, coachable, and eager to learn from real systems in production.
• ● Able to work through ambiguity with guidance and grow ownership over time.