Job Type: Contract (W2 Only)
Contract Length: 6+ months
Pay Range: $60 - $65/hr
Start Date: ASAP
Location: Foster City, CA
About the Opportunity:
Our client, a leader in autonomous vehicle technology, is looking for a skilled PHM Monitor Development Contractor to join their team for a 6+ month engagement. This project involves designing, developing, and deploying offline health monitoring algorithms and prognostic models for a fleet of autonomous vehicles. This is a high-impact role that requires a self-motivated professional who can hit the ground running to help predict component failures, estimate Remaining Useful Life (RUL), and optimize preventative maintenance schedules.
Key Responsibilities & Deliverables:
This role is focused on the successful completion of specific tasks and deliverables. Your responsibilities will include:
- Model Design: Designing and training data-driven and physics-based prognostic models to detect faults and estimate the Remaining Useful Life (RUL) of critical hardware components.
- Algorithm Development: Developing offline diagnostic algorithms to detect anomalies, wear-and-tear patterns, and early fault indicators using batch telemetry, sensor logs, and historical maintenance data.
- Data Engineering: Performing large-scale ETL and data manipulation using PySpark on Databricks clusters, and engineering, packaging, and deploying models using production-grade pipelines.
- Validation & Testing: Rigorously back-testing prognostic models against historical failure data to ensure high accuracy, low false-positive rates, and reliability.
- Deployment & Reporting: Designing fleet result dashboards, developing robust alerting strategies, and providing clear technical documentation of model architectures and deployment procedures.
We are looking for someone with a proven track record of successful contract engagements. The ideal candidate will have:
- 3+ years of experience specifically focused on Prognostics and Health Management (PHM), predictive maintenance, or reliability engineering.
- A BS degree in Mechanical Engineering, Electrical Engineering, Data Science, or a related field.
- Expertise in PySpark and Python for large-scale data manipulation, ETL, and feature engineering.
- Hands-on experience working with the Databricks platform for development and deployment, and scheduling analytical workflows using Airflow DAGs.
- Proficiency in anomaly detection, time-series forecasting, survival analysis, and ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
- Familiarity with applying signal processing techniques (e.g., FFT, wavelet transforms) and understanding hardware mechanics/failure modes (FMEA/FMECA).
- Demonstrated ability to work autonomously and manage your own time effectively to meet project goals.
- Strong communication skills to provide clear technical documentation and status updates.
- W2 only (No C2C or 1099 contractors)
