Senior / Mid AI Engineer - Time Series

Enzo is building AI systems that detect water leaks early, before damage happens. Our models process millions of time-series sensor data points from real buildings every day, turning noisy signals into actionable risk insights for insurers and property owners.

As an AI Engineer, you’ll strengthen our AI team by improving model quality, speed of iteration, and technical depth. You’ll work hands-on with existing AI engineers, lead modeling decisions, and continuously bring new ideas into the system.

This role is about better models, better learning, and real-world performance, not research for its own sake.

You’ll own and advance the core AI logic behind Enzo’s leakage-detection system. Your mission is to improve detection quality, robustness, and performance under real-world constraint, while helping junior engineers grow and move faster.

You will actively track relevant research, evaluate new approaches, and decide what is worth implementing, always with production impact in mind.

• Design, train, and evaluate time-series models (and others) for water leakage and anomaly detection and automate work processes.
• Improve robustness to noise, drift, and real-world data imperfections.
• Balance model complexity, accuracy, and inference performance.

• Work closely with other AI engineers on model design and experimentation.
• Review approaches, challenge assumptions, and answer technical questions.
• Help establish strong standards for experimentation and evaluation.

• Track relevant arXiv papers, applied ML research, and industry developments.
• Translate promising ideas into pragmatic experiments.
• Distinguish signals from hype, focus on what actually improves outcomes.

• Optimize training and inference performance.
• Push for faster iteration cycles and clearer experimental conclusions.
• Always ask: does this materially improve detection in production?

• Work closely with MLOps and Engineering to ensure models are production-ready.
• Design models with deployment, monitoring, and scaling in mind.

• Experience building and improving ML models beyond toy problems.
• Strong understanding of time-series analysis, anomaly detection, and signal processing.
• Solid foundation in ML algorithms and statistical reasoning.

• Strong Python skills.
• Experience with ML frameworks (e.g. PyTorch, TensorFlow, or similar).
• Comfortable working with large, real-world datasets.

• You actively follow AI research and news.
• You can explain why an approach is worth trying, and why another isn’t.

• You move fast and iterate pragmatically.
• You take ownership instead of waiting for perfect clarity.
• You enjoy working close to real data, real customers, and real constraints.

• Fluent in English