Machine Learning Engineer, Predictive Maintenance
Builds and deploys machine-learning models for predictive maintenance of industrial machinery, extracting fault signatures from vibration and other IIoT sensor data. The role owns end-to-end ML solutions — signal processing, CNN/RNN/LSTM model development, and production pipelines on AWS (SageMaker, S3, Athena, Spark).
- Compensation
- $151K–$175K / yr
- Location
- United States
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Full time
- Experience
- Not specified
- Category
- Ml / AI
- Posted
Skills
Must have
- 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 signal processing techniques (FFT, STFT, Time Synchronous Averaging, Wavelet Transform, Spectral Kurtosis, Spectral Correlatio
- Strong foundations in Machine Learning: classical ML, supervised/unsupervised learning, anomaly detection, feature engineering, ANNs, CNNs, RNNs/LSTMs, attention mechanisms
- Strong understanding of time-series modeling and evaluation
- Hands-on experience architecting and delivering production ML solutions on AWS (SageMaker, S3, Athena, Glue, EMR, Lambda, Step Functions, ECS/Fargate, Timestream, Aurora/RDS, Bedrock, IoT Core, CloudW
- Data and ML engineering at scale, including distributed processing with Spark (PySpark) or equivalent
- Experience designing data mining and annotation workflows with domain experts (label taxonomy, labeling tooling, label QA)
- Strong software engineering and programming skills in Python: modular, testable, maintainable production code; Git, code review, unit/integration/regression testing, CI/CD
- Proficiency in scientific and ML/DL frameworks such as NumPy, Pandas, SciPy, scikit-learn, TensorFlow, PyTorch
- Working knowledge of SQL (MySQL/Aurora, Athena)
- Excellent communication and teamwork skills, collaborating with domain experts, MLOps, Data Engineers, and Product
Nice to have
- Experience using LLMs, AI-assisted coding, and agentic AI tools; designing/integrating LLM- or agent-based workflows
- Experience building benchmark datasets, validating models against ground truth, and creating repeatable evaluation/regression workflows
- Physics-informed ML and experience with additional modalities (electrical/current signature analysis, ultrasound, oil analysis)
- Demonstrated research work in high-quality journals or significant applied work on related subjects
Full description
AssetWatch serves global manufacturers by powering manufacturing uptime through the delivery of an unparalleled condition monitoring experience, with a passion to care about the assets our customers care for every day. We are a devoted and capable team that includes world-renowned engineers and distinguished business leaders united by a common goal – To build the future of predictive maintenance. As we enter the next phase of rapid growth, we are seeking people to help lead the journey.
JOB SUMMARY
We are seeking an experienced Machine Learning Engineer with a specialized background in Signal Processing and Predictive Maintenance. The ideal candidate will have a talent for extracting diagnostic features and fault signatures from IIoT sensors, including accelerometers, temperature sensors, and electrical signals, as well as other static and contextual machine data such as images and equipment metadata. The successful candidate will utilize this data to develop and implement algorithms tailored for the diagnosis, prognosis, anomaly detection, and health assessment of critical machinery components, including bearings, gearboxes, shafts, motors, pumps, fans, belts, and more.
This crucial role requires a thorough understanding of classical Machine Learning, Artificial Neural Networks, and Deep Learning algorithms, including CNN and Sequence-based Models such as RNNs and LSTMs enhanced with attention mechanisms. In addition, expertise in supervised and unsupervised learning, anomaly detection, ranking and decision models, feature engineering, and leveraging Large Language Models (LLMs) and agentic AI is a must. This is a hands-on role that also carries solution-architect responsibility: the candidate is expected to design end-to-end ML solutions — from data access and large-scale feature computation through training, inference, and production monitoring — on AWS, using services such as SageMaker (Training, Processing, Pipelines, Endpoints), S3, Athena, AWS Glue, EMR/Spark, Lambda, Step Functions, ECS/Fargate, Timestream, Aurora/RDS, Amazon Bedrock, and CloudWatch. Strong software engineering fundamentals and data/ML engineering at scale (distributed processing with Spark or equivalent) are also essential for building maintainable, testable, and production-ready ML systems. While mastery in algorithm and model development is a must, experience developing and deploying these solutions within the AWS cloud environment and collaborating closely with MLOps and Data Engineering teams is required.
ESSENTIAL 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.