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Machine Learning Engineer, Predictive Maintenance

47 minutes ago
United States
Full-time
Senior
Machine Learning Engineer
Software Development
Apache Spark AWS AWS CDK CI/CD Deep Learning Docker Feature Engineering Git Machine Learning MLOps MySQL NumPy Pandas Python PyTorch Scikit-learn SciPy SQL TensorFlow Terraform
Apply Now
AssetWatch®

AssetWatch®

AssetWatch® is a leading provider of predictive maintenance solutions for industrial plants. Their cloud-based software enables proactive detection and resolution of machine issues, reducing downtime and increasing plant reliability. By combining vibra...

Construction & Engineering
51-250
Founded 2014
View All Jobs 26

Description

  • Extract and analyze vibration, temperature, electrical, image, and machine-context data to identify diagnostic features and fault signatures.
  • Select, train, validate, and deploy models for machinery diagnosis, prognosis, anomaly detection, and health assessment.
  • Develop time-, frequency-, and context-domain features for bearing, gear, motor, pump, fan, belt, and other equipment faults.
  • Architect end-to-end ML solutions covering data access, feature computation, storage, orchestration, inference, scalability, and cost.
  • Build distributed data and feature pipelines using Spark, EMR, Glue, SageMaker Processing, Athena, and Parquet-based data layouts.
  • Design data mining, annotation, benchmarking, validation, and regression workflows with condition-monitoring experts.
  • Develop modular, tested, maintainable Python ML software and production pipelines.
  • Collaborate with MLOps and Data Engineering teams on deployment, CI/CD, model versioning, infrastructure as code, monitoring, and troubleshooting.
  • Evaluate model performance across machines and operating conditions, investigate errors, and communicate findings to stakeholders.

Requirements

  • Master’s or Ph.D. in Mechanical Engineering, Electrical Engineering, Computer Science/Engineering, or a related field.
  • Proven predictive-maintenance and condition-monitoring experience using signal-processing methods such as FFT, STFT, TSA, wavelets, spectral kurtosis, spectral correlation, envelope analysis, and Hilbert transforms.
  • Strong knowledge of classical ML, supervised and unsupervised learning, anomaly detection, feature engineering, CNNs, RNNs, LSTMs, and attention mechanisms.
  • Experience with time-series modeling and selecting methods based on physical problems, data quality, ground truth, and operational requirements.
  • Hands-on experience architecting production ML solutions on AWS, including SageMaker, S3, Athena, Glue, EMR, Lambda, Step Functions, ECS/Fargate, Timestream, Aurora/RDS, Bedrock, CloudWatch, and IAM.
  • Experience with distributed processing using Spark or PySpark and large time-series or binary signal datasets.
  • Strong Python software-engineering skills, including Git, code review, testing, debugging, CI/CD, and maintainable production code.
  • Proficiency with NumPy, Pandas, SciPy, scikit-learn, TensorFlow, PyTorch, or similar libraries; working knowledge of SQL is required.
  • Experience with LLMs, AI-assisted coding, or agentic AI is desirable; physics-informed ML and additional modalities such as current signatures, ultrasound, or oil analysis are a plus.
  • Excellent communication and collaboration skills; applied research or high-quality journal publications are a plus.

Benefits

  • US base salary of $151,000–$175,000, plus equity, benefits, and potential variable compensation.
  • Remote-first work across the United States and Ontario, Canada, with collaboration during core working hours.
  • Flexible work schedule and unlimited PTO.
  • Comprehensive benefits, including a retirement plan match.
  • Stock options and a competitive compensation package.

Interested in this position?

Apply directly on the company website

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