Member of Technical Staff, Machine Learning

About the Company

There are over 5 billion users using basic applications today such as email, notes, and tasks that are not AI-native. Our mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organizing, 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 with over ~90% reduced time.

About the 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.

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.

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.

Tech Stack

• Python
• PyTorch / JAX
• Production ML systems running on GPUs

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 a truly magical product in the hands of our users.