[Hiring] Machine Learning Engineer @AssetWatch, Inc.

Role Description

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
• Electrical signals
• 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:

• Diagnosis
• Prognosis
• Anomaly detection
• Health assessment of critical machinery components, including:
• Bearing
• Gearbox
• Shaft
• Motor
• Pump
• Fan
• Belt

This crucial role requires a thorough understanding of:

• Classical Machine Learning
• Artificial Neural Networks
• Deep Learning algorithms, including CNN and Sequence-based Models such as RNNs and LSTMs enhanced with attention mechanisms
• Supervised and unsupervised learning
• Anomaly detection
• Ranking and decision models
• Feature engineering
• Leveraging Large Language Models (LLMs) and agentic AI

This is a hands-on role that also carries solution-architect responsibility:

• 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
• 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.
• Experience developing and deploying these solutions within the AWS cloud environment and collaborating closely with MLOps and Data Engineering teams is required.

Qualifications

• 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.
• 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.
• Hands-on experience architecting and delivering production ML solutions on AWS is required.
• Demonstrated data and ML engineering at scale, including distributed processing with Spark (PySpark) or equivalent.
• Experience designing data mining and annotation workflows in partnership with domain experts.
• Strong software engineering and programming skills in Python.
• Proficiency in scientific and ML/DL frameworks and libraries such as NumPy, Pandas, SciPy, scikit-learn, TensorFlow, PyTorch, or similar tools.
• Experience using Large Language Models, AI-assisted coding, and agentic AI tools for software and ML development.
• Working knowledge of SQL (MySQL/Aurora, Athena).
• Experience building representative benchmark datasets and validating models against reviewed ground truth.
• Physics-informed ML and experience with additional modalities (electrical/current signature analysis, ultrasound, oil analysis) are a plus.
• Excellent communication and teamwork skills.
• Demonstrated research work in high-quality journals or significant applied work on related subjects is a plus.

Requirements

• Extract, preprocess, and analyze data from various sensor sources, primarily machine vibration.
• Determine the appropriate modeling techniques for each problem.
• Train, validate, and deploy predictive maintenance models.
• Develop and evaluate time-domain, frequency-domain, and contextual features.
• Act as the solution architect for assigned ML initiatives.
• Build and scale distributed data and feature pipelines over large historical sensor datasets.
• Design and run data mining and annotation workflows.
• Build and maintain benchmark, training, validation, and regression datasets.
• Design experiments and compare new algorithms against existing production methods.
• Develop robust, modular, reusable, and maintainable ML software in Python.
• Use modern AI-assisted and agentic coding approaches.
• Collaborate with the research and engineering team.
• Work closely with MLOps and Data Engineering teams.
• Stay updated with the latest advancements in Machine Learning, Deep Learning, Signal Processing, agentic AI, and industrial condition monitoring.
• Present findings, model performance, strategies, tradeoffs, and solutions to other teams and stakeholders.

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