Member of Technical Staff, Machine Learning

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. Role 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. Focus 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 Python 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. Bias toward shipping, iteration, and continuous improvement. Outcomes ML models in production meet expected accuracy, latency, and reliability targets. Production issues are identified quickly, debugged effectively, and root causes addressed. Data pipelines, training loops, and inference systems are robust, reproducible, and maintainable. Collaborates effectively with engineers, product, and research teams to deliver reliable ML-powered features. Iterations on models and systems are driven by real-world signals and measurable improvements. How We Work The best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product #J-18808-Ljbffr