Machine Learning Engineer, Predictive Maintenance
United States · USD 151,000-175,000 · Hybrid · Posted Oct 1, 2026
At a glance
- Workplace
- Hybrid
- Location
- United States
- Salary
- USD 151,000-175,000 · Base salary
About the role
- AssetWatch is seeking an experienced Machine Learning Engineer with a specialized background in Signal Processing and Predictive Maintenance to join their remote-first team.
- The role involves extracting diagnostic features and fault signatures from IIoT sensors and developing algorithms for the diagnosis, prognosis, anomaly detection, and health assessment of critical machinery components.
- This is a hands-on position that also carries solution-architect responsibility, requiring end-to-end ML solution design on AWS.
Responsibilities
- Extract, preprocess, and analyze data from various sensor sources, primarily machine vibration, while also working with temperature, electrical, image, and machine-context data 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, signal-processing methods, and hybrid or rules-based approaches.
- Train, validate, and deploy predictive maintenance models to accurately identify and predict machinery faults such as bearing, gearbox, shaft, motor, pump, fan, and belt faults.
- Develop and evaluate time-domain, frequency-domain, and contextual features, including harmonics, sidebands, envelope-spectrum features, running-speed characteristics, bearing and gear-mesh fault frequencies, and other fault-specific evidence.
- Act as the solution architect for assigned ML initiatives by defining and documenting end-to-end design before large-scale execution.
- Build and scale distributed data and feature pipelines over large historical sensor datasets using Spark, Athena, and Parquet-based data layouts.
- Design and run data mining and annotation workflows that connect Condition Monitoring Engineers with Data Science.
- Build and maintain benchmark, training, validation, and regression datasets using real machine cases to establish reliable ground truth and measurable model acceptance criteria.
- Design experiments and compare new algorithms against existing production methods, investigating false positives, false negatives, suppression behavior, data-quality issues, and performance across different machines and operating conditions.
- Develop robust, modular, reusable, and maintainable ML software in Python, including production-quality model code, shared libraries, configuration, unit and regression tests, code reviews, refactoring, debugging, and version-controlled development workflows.
Requirements
- 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, and Hilbert Transform.
- Strong foundations in Machine Learning and model development, including classical ML algorithms, supervised and unsupervised learning, anomaly detection, feature engineering, Artificial Neural Networks, CNNs, sequence-based models like RNNs and LSTMs, and attention mechanisms.
- Strong understanding of time-series modeling and evaluation.
- Hands-on experience architecting and delivering production ML solutions on AWS, including services such as SageMaker, S3, Athena, AWS Glue, EMR, Lambda, Step Functions, ECS/Fargate, Timestream, Aurora/RDS, Amazon Bedrock, IoT Core, CloudWatch, and IAM.
- Demonstrated data and ML engineering at scale, including distributed processing with Spark or equivalent.
- Experience designing data mining and annotation workflows in partnership with domain experts.
- Strong software engineering and programming skills in Python, with experience developing modular, testable, maintainable production code, Git, code review, unit/integration/regression testing, debugging, CI/CD, and reproducible development practices.
- Proficiency in scientific and ML/DL frameworks and libraries such as NumPy, Pandas, SciPy, scikit-learn, TensorFlow, and PyTorch.
- Working knowledge of SQL and the ability to independently explore, validate, and troubleshoot large and imperfect industrial datasets.
Preferred qualifications
- Experience using Large Language Models, AI-assisted coding, and agentic AI tools for software and ML development
- Physics-informed ML and experience with additional modalities such as electrical or current signature analysis, ultrasound, and oil analysis
- Experience building representative benchmark datasets, validating models against reviewed ground truth, analyzing false positives and false negatives, and creating repeatable evaluation or regression workflows
- Demonstrated research work in high-quality journals or significant applied work on related subjects
Benefits
- Competitive compensation package including stock options
- Flexible work schedule
- Comprehensive benefits including retirement plan match
- Opportunity to make a real impact every day
- Work with a dynamic and growing team
- Unlimited PTO
Workplace
- Remote-first company with a distributed team across the United States and Ontario, Canada.
- Collaboration within core working hours is required.
Skills
- Python
- AWS
- SageMaker
- Spark
- PySpark
- TensorFlow
- PyTorch
- SQL
- Docker
- Terraform
- NumPy
- Pandas
Show all 15 skills
- SciPy
- Scikit-learn
- Git
