As a Machine Learning Engineer, you will focus on analyzing time series data, particularly vibration and acoustic signals, to develop models for fault detection, diagnosis, and RUL estimation. You will work closely with a multidisciplinary team of data scientists, engineers, and domain experts to design and implement machine learning algorithms that enhance our predictive maintenance capabilities.

Key Responsibilities

• Analyze and preprocess time series data from vibration and acoustic sensors to extract meaningful features for fault detection and prognostics.
• Develop and implement machine learning models for anomaly detection, clustering, and classification to identify equipment faults and predict RUL.
• Apply signal processing techniques to enhance data quality and feature extraction processes.
• Collaborate with domain experts to interpret model results and integrate insights into practical maintenance strategies.
• Validate and test models using historical data and real-time sensor inputs to ensure robustness and accuracy.
• Document methodologies, experiments, and findings to support knowledge sharing and continuous improvement.
  
  

Qualifications

• Bachelor’s degree in Computer Science, Electrical Engineering, Mechanical Engineering, or a related field.
• Fundamental understanding of machine learning concepts and algorithms.
• Basic experience with anomaly detection techniques, clustering methods, and signal processing.
• Proficiency in programming languages such as Python or MATLAB.
• Familiarity with data analysis and visualization tools.
• Strong problem-solving skills and the ability to work collaboratively in a team environment.
• Excellent communication skills to effectively convey technical concepts.
  
  

Preferred Skills

• Exposure to time series analysis and experience working with sensor data.
• Knowledge of predictive maintenance concepts and condition monitoring practices.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Understanding of industrial equipment and maintenance processes.
  
  

Benefits

• Competitive salary and performance-based incentives.
• Opportunities for professional growth and development.
• Collaborative and inclusive work environment.
• Exposure to cutting-edge technologies in the field of predictive maintenance.
• Health and wellness benefits.
  
  

Skills: node.js,matlab,data visualization,php,next.js,tensorflow,machine learning,python,javascript,api integration,vue.js,web,time series analysis,anomaly detection,data analysis,webflow,seo,pytorch,git,react,clustering,deep learning,google analytics,css,html,signal processing,wordpress