Machine Learning Engineer (Predictive Maintenance)

Join our team as a Machine Learning Engineer specializing in Predictive Maintenance. In this role, you will extract, preprocess, and analyze data from various sensor sources to identify and enhance diagnostic features. You will determine the appropriate modeling techniques for each problem, train and deploy predictive maintenance models, and act as the solution architect for assigned ML initiatives. You will also build and scale distributed data and feature pipelines, design and run data mining and annotation workflows, and develop robust ML software in Python. Additionally, you will collaborate with the research and engineering team, work closely with MLOps and Data Engineering teams, and stay updated with the latest advancements in the field.

• Extract, preprocess, and analyze data from various sensor sources to identify and enhance diagnostic features and fault-specific signatures.
• Determine the appropriate modeling techniques for each problem, including classical Machine Learning, supervised and unsupervised learning, anomaly detection, Artificial Neural Networks, Deep Learning, and hybrid approaches.
• Train, validate, and deploy predictive maintenance models to accurately identify and predict machinery faults, including CNNs, RNNs, LSTMs, and other time-series approaches.

• Experience building representative benchmark datasets, validating models against reviewed ground truth, analyzing false positives and false negatives, and creating repeatable evaluation or regression workflows is highly desirable
• Hands-on experience architecting and delivering production ML solutions on AWS is required, including several of: SageMaker (Training, Processing, Pipelines, Model Registry, Endpoints), S3, Athena, AWS Glue, EMR, Lambda, Step Functions, ECS/Fargate, Timestream, Aurora/RDS, Amazon Bedrock, IoT Core, CloudWatch, and IAM
• Physics-informed ML and experience with additional modalities (electrical/current signature analysis, ultrasound, oil analysis) are a plus
• Experience designing data mining and annotation workflows in partnership with domain experts, including label taxonomy design, labeling tooling, label quality control, and converting noisy expert feedback into model-ready ground truth
• Proficiency in scientific and ML/DL frameworks and libraries such as NumPy, Pandas, SciPy, scikit-learn, TensorFlow, PyTorch, or similar tools
• Working knowledge of SQL (MySQL/Aurora, Athena) and the ability to independently explore, validate, and troubleshoot large and imperfect industrial datasets
• Experience using Large Language Models, AI-assisted coding, and agentic AI tools for software and ML development; familiarity with designing or integrating LLM- or agent-based workflows for data processing, analysis, decision support, or automation is highly desirable
• Strong understanding of time-series modeling and evaluation, including the ability to select appropriate techniques based on the physical problem, data characteristics, available ground truth, and operational requirements
• Strong software engineering and programming skills in Python, with experience developing modular, testable, maintainable production code; proficiency with Git, code review, unit/integration/regression testing, debugging, CI/CD, and reproducible development practices on noisy, large, imperfect industrial datasets
• Demonstrated research work in high-quality journals or significant applied work on related subjects is a plus
• Master's or Ph.D. in Mechanical Engineering, Electrical Engineering, Computer Science/Engineering, or a related field
• Proven experience in Predictive Maintenance and Condition Monitoring with a focus on Signal Processing techniques such as FFT, Short Time Fourier Transform, Time Synchronous Averaging, Wavelet Transform, Spectral Kurtosis, Spectral Correlation, Envelope Analysis, Hilbert Transform, and related methods
• Strong foundations in Machine Learning and model development, including classical Machine Learning algorithms, supervised and unsupervised learning, anomaly detection, feature engineering, Artificial Neural Networks, Convolutional Neural Networks, Sequence-based Models such as RNNs and LSTMs, and Attention Mechanisms
• Demonstrated data and ML engineering at scale, including distributed processing with Spark (PySpark) or equivalent, efficient handling of large time-series and binary signal data, and pragmatic reasoning about compute cost, runtime, and reprocessing risk
• Excellent communication and teamwork skills, including the ability to collaborate with condition-monitoring domain experts, MLOps engineers, Data Engineers, Product, and other stakeholders