Sr. Machine Learning Engineer, Predictive Maintenance

AssetWatch, Inc.

1d•Remote

About The Position

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. AssetWatch is seeking a Senior Machine Learning Engineer to advance the state of predictive maintenance across industrial systems. This role focuses on building interpretable, production-grade machine learning solutions grounded in deep domain expertise in vibration analysis and condition monitoring. As a senior individual contributor, you will own complex modeling initiatives while setting technical standards for data processing, signal analysis, and model design. You will work closely with reliability engineers, other data scientists, and MLOps partners to deliver solutions that are trusted, explainable, and operationally robust in real-world industrial environments.

Requirements

Significant experience (typically 5+ years) applying signal processing techniques to noisy sensor data.
Strong experience (typically 4+ years) building and deploying machine learning models for time-series analysis, anomaly detection, or diagnostics, with a strong emphasis on interpretability.
Strong experience (typically 4+ years) designing and maintaining data processing pipelines for large-scale sensor or time-series data, including data quality and validation.
Significant (typically 5+ years) professional Python experience.
Proven experience in vibration analysis and fault detection of industrial systems and equipment (e.g., rotating machinery such as pumps, gearboxes, or electric motors).
Experience deploying and supporting ML models in production environments.
Familiarity with cloud platforms (AWS), Docker, and SQL databases.
Demonstrated ability to independently drive complex technical initiatives end to end.
Strong written and verbal communication skills for cross-functional collaboration.
Ownership mindset aligned with a senior individual contributor role.

Responsibilities

Develop interpretable machine learning models for anomaly detection, fault classification, and failure prediction using industrial sensor data.
Apply time-domain and frequency-domain signal processing techniques to extract physically meaningful features from vibration signals.
Embed condition monitoring and reliability domain knowledge into feature engineering and modeling decisions.
Partner with reliability engineers to validate model outputs against known failure modes.
Design and maintain scalable data processing pipelines for ingesting, cleaning, transforming, and validating large-scale time-series data.
Build and refine interpretable machine learning models, leveraging classical methods and domain-informed approaches alongside modern techniques where appropriate.
Optimize models for reliable operation in production environments, including performance monitoring, drift detection, and retraining strategies.
Partner with MLOps and platform teams to integrate models into scalable, maintainable production systems.
Define evaluation frameworks and metrics that reflect both predictive accuracy and practical utility in maintenance decision-making.
Create dashboards and alerts that provide actionable intelligence to stakeholders.
Set best practices for modeling, data processing, and experimental rigor within the team.
Document system behavior and modeling decisions for internal stakeholders.
Provide technical mentorship and guidance through design and code reviews.