Job#: 3056292 Job Description: PHM Monitor Development Engineer
Location: Foster City, California (Onsite)
Role Overview
We are seeking a skilled Prognostics & Health Monitoring (PHM) Engineer to design, develop, and deploy offline health monitoring algorithms and prognostic models for a fleet of autonomous vehicles. In this role, you will focus on the backend development and deployment of algorithms that analyze historical and batch telemetry data. Your work will enable maintenance teams to predict component failures, estimate Remaining Useful Life (RUL), and optimize preventative maintenance schedules.
Key Responsibilities
- Define technical requirements for new health monitors by identifying the problem, required telemetry data, and validation data.
- Design and train data-driven and/or physics-based prognostic models to detect faults and estimate the Remaining Useful Life (RUL) of critical hardware components.
- Develop offline diagnostic algorithms to detect anomalies and early fault indicators using batch telemetry and historical maintenance data.
- Clean, filter, and extract relevant features from large volumes of time-series sensor data.
- Perform large-scale ETL and data manipulation using PySpark on Databricks clusters.
- Engineer, package, and deploy developed models using production-grade pipelines.
- Design and implement fleet result dashboards to visualize monitor output.
- Develop a robust alerting strategy and configure alert delivery.
- Rigorously back-test prognostic models against historical failure data to ensure accuracy and reliability.
- Provide clear technical documentation of model architectures, deployment procedures, and codebases.
Required Qualifications
Education: A BS Degree in Mechanical Engineering, Electrical Engineering, Data Science, or a related field is required.
Experience: 3+ years of experience focused on Prognostics and Health Management (PHM), predictive maintenance, or reliability engineering is necessary.
Technical Skills:- Expertise in PySpark and Python for large-scale data manipulation, ETL, and feature engineering.
- Hands-on experience with the Databricks platform for development, deployment, and dashboard creation.
- Experience deploying and scheduling analytical workflows using Airflow DAGs.
- Proficiency with version control using Git.
- Understanding of anomaly detection, time-series forecasting, survival analysis, and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Experience applying signal processing techniques to raw sensor data.
- Familiarity with deploying batch-processing or offline analytical scripts.
- Understanding of hardware mechanics, fatigue, degradation models, or failure modes (FMEA/FMECA).
Preferred Qualifications
- Prior experience in the Automotive or Aerospace sector.
- Experience working with large-scale data storage and querying (SQL, Databricks, Spark, etc.).
- A Master's or Ph.D. in Mechanical Engineering, Electrical Engineering, Data Science, or a related field.
Everforth Apex is a world-class IT services company that serves thousands of clients across the globe. When you join Everforth Apex, you become part of a team that values innovation, collaboration, and continuous learning. We offer quality career resources, training, certifications, development opportunities, and a comprehensive benefits package. Our commitment to excellence is reflected in many awards, including ClearlyRateds Best of Staffing in Talent Satisfaction in the United States and Great Place to Work in the United Kingdom and Mexico.
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