Responsibilities
- Develop predictive maintenance algorithms using machine learning techniques for time-series data.
- Analyze sensor data streams to identify patterns that predict equipment failure.
- Research and stay up to date with academic literature and state-of-the-art condition monitoring techniques, translating relevant advances into practical solutions.
- Collaborate with engineers to improve data pipelines and enhance model accuracy.
- Build scalable, real-time models for low-latency predictions.
- Create diagnostic tools that enable data-driven maintenance decisions.
- Work with the laboratory team to design experiments and develop failure datasets using real machinery to validate hypotheses, develop new models, and optimize existing ones.
- Continuously refine models based on real-world performance, experimental results, and feedback.
Requirements
- Expertise in machine learning, time-series analysis, and anomaly detection.
- Proficiency in Python and common data science and ML libraries (e.g., NumPy, pandas, scikit-learn, PyTorch).
- Solid understanding of signal processing concepts and hands-on experience with industrial sensor data (e.g., vibration, current, temperature, pressure).
- Ability to read, interpret, and apply insights from academic literature and state-of-the-art research in condition monitoring and fault diagnosis.
- Experience designing experiments to validate hypotheses and benchmark models.
- Strong problem-solving skills and ability to handle noisy, high-dimensional data.
- Advanced English.
Bonus Points
- Familiarity with both academic research and real-world applications in condition monitoring, fault diagnosis, and prognostics (e.g., vibration-based methods, model-based vs. data-driven approaches).
- Experience translating academic methods into robust, production-ready algorithms.
- Prior experience working in industrial or manufacturing environments.
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What We Do
Tractian is a machine intelligence company that offers industrial monitoring systems. Tractian builds streamlined hardware-software solutions to give maintenance technicians and industrial decision-makers comprehensive oversight of their operations. It is democratizing access to sophisticated real-time monitoring and asset operations tools.
Tractian's solutions are used in environments that address a combined total of 5% of global industrial output. The company’s broad market reach is evidenced in its customer base from various industries, such as John Deere, Procter & Gamble, Caterpillar, Goodyear, Carrier, Johnson Controls, and Bimbo, the owner of the brands Little Bites and Thomas Bagels. Tractian's customers see a 6-12x ROI with savings of $6,000 per monitored machine annually on average.
In a major milestone and a first for the industry, Tractian launched the AI-Assisted Maintenance category in the industrial sector. In this new paradigm, artificial intelligence identifies machine problems and suggests preventive actions to be taken, giving invaluable insight and support to maintenance professionals. It is important to highlight that the intent of Assisted Maintenance is firmly rooted in augmenting maintenance professionals to provide more assertive diagnosis with human-in-the-loop feedback.
Tractian's mission is to elevate this category of workers in a highly impactful way. The Assisted Maintenance category will provide unimaginable support for maintenance professionals. By combining shop floor expertise with our technology, maintainers will be able to anticipate and address issues with unprecedented accuracy and speed
