About The Position

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.

Requirements

  • 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.
  • Bias toward shipping, iteration, and continuous improvement.

Responsibilities

  • 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.
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