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Fetcherr builds responsible AI that transforms market complexity into measurable profit growth. The company's core technology is the Market Model—a proprietary AI-powered system delivering 96% forecast accuracy and real-time decision intelligence for commercial teams. Built on a glass-box architecture using market data with full transparency, it has been deployed across global aviation with corporate partners including Delta, Virgin Atlantic, WestJet, and others, delivering an average 7% profit uplift.
The Large Market Modeling (LMM) team is the engine behind Fetcherr's pricing intelligence. This team builds and trains the models that transform market signals, customer behavior, and competitive dynamics into reliable, production-ready demand models.
You will be a Senior ML Engineer supporting and maintaining the company's machine learning capabilities. This role focuses on production systems, ensuring reliability, scalability, and explainability of models while enabling research teams to deliver impact faster.
Key responsibilities include:
- Collaborating with cross-functional teams to keep ML systems robust, explainable, and aligned with business needs
- Monitoring and reporting on ML model performance, reliability, and explainability metrics
- Participating in model retraining and implementing automation and optimization of MLOps pipelines
- Extending and scaling monitoring pipelines to support new features in development
- Investigating, troubleshooting, and resolving issues in production ML workflows, from initial triage to root-cause analysis with model owners
- Developing and maintaining repositories for feature engineering, inference monitoring pipelines, and artifact monitoring tools
- Performing exploratory data analysis on historical datasets to identify quality issues and maintain data health
- Implementing and overseeing production-based adjusters across customer deployments
- Evaluating and tracking critical ML artifacts such as explainability files and coverage metrics
- Supporting development and maintenance of internal tools including interfaces, registries, and feature monitoring frameworks
- Building and maintaining static and temporal features, including seasonality, event-based, and price-related features
This is an ideal role for someone who enjoys working closely with production systems and has a detail-oriented, collaborative approach to ensuring ML reliability and explainability.
