machine learning engineer for a proactive AI assistant

The company is an early-stage AI startup building a proactive assistant for everyday productivity tools such as email, notes, and tasks. Its focus is on reliable long-running workflows, persistent context, and completing tasks despite the non-deterministic nature of underlying models.

• Build core machine learning systems powering a proactive, long-horizon AI assistant;
• Own ML initiatives end to end, including data preparation, training, evaluation, inference, deployment, and iteration;
• Turn research ideas into practical systems that operate reliably in production;
• Investigate model failures and system issues using real-world production signals;
• Continuously ship, measure results, identify improvements, and refine the system;
• Collaborate with research, product, and engineering teams to deliver meaningful user impact;
• Mentor other machine learning engineers and provide technical guidance through code reviews and strong engineering practices;
• Design solutions accounting for production constraints such as latency, cost, reliability, efficiency, and safety.

• Experience building and shipping machine learning systems used by real users;
• Strong understanding of how modern machine learning models behave and fail in production environments;
• Excellent Python programming skills and experience with PyTorch and/or JAX;
• Experience with GPU-based model training and inference systems;
• Ability to write reliable, maintainable, production-quality code;
• Strong systems thinking and ability to design solutions beyond experimental scripts or research prototypes;
• Ability to work independently, take full ownership, and complete complex initiatives;
• Strong problem-solving skills and confidence working with incomplete or ambiguous information;
• Clear communication skills;
• Fluent English.

• Cash and equity compensation;
• Flexible hours in a distributed, global team;
• Generous paid time off;
• Company laptop provided;
• Hiring process includes 3, occasionally 4, interviews, followed by fast decisions.