TSFM Expert

Company Description Infinite Uptime is a production outcomes partner for heavy industries, helping customers improve maintenance efficiency, reduce conversion costs, and increase throughput. Its flagship product, PlantOS™, is a prescriptive AI orchestration system and one of the most operator-validated vertical AI platforms in the sector. The platform currently drives live outcomes across 1,000+ plants in 9 industrial verticals and 28 countries. With 99.97% prediction accuracy and 90%+ of prescriptions acted upon, every outcome is validated and signed off by plant operators. Infinite Uptime focuses on turning predictions into real, measurable operational results.Role Description The TSFM Expert will work as a full-time, remote contributor supporting heavy industry customers in using PlantOS™ to achieve tangible production outcomes. In this role, the TSFM Expert will interpret AI-driven insights, translate them into actionable maintenance and operations strategies, and collaborate closely with plant operators and engineering teams. Daily activities include reviewing system alerts, validating predictions, fine-tuning TSFM (technical, systems, and failure management) models, and documenting operator feedback to improve prescriptions. The role also involves creating clear reports, leading remote reviews and training sessions, and partnering with internal product and data teams to optimize outcome orchestration. The TSFM Expert will be expected to maintain strong customer relationships, ensure operator sign-off on outcomes, and contribute to continuous improvement of PlantOS™ performance.QualificationsPractical expertise in condition monitoring, reliability engineering, and asset management for heavy industry environments.·       Develop and adapt modern deep-learning models for time-series on our proprietary industrial sensor data.·       Build retraining and adaptation pipelines so models stay accurate as live data streams evolve.·       Apply techniques that enable fast adaptation to new sensor sources and equipment types with limited labeled data.·       Drive measurable accuracy gains across our asset-class specific models.