Overview Placement Type:

Temporary Salary:

$61.76-68.62 Hourly W2

Start Date:

Aug 11, 2026 At Aquent, we partner with groundbreaking companies that are redefining industries. We are currently collaborating with a visionary leader in autonomous technology, dedicated to creating safe, sustainable, and accessible transportation solutions. This company is at the forefront of innovation, building a future where mobility is seamless and intelligent. Join a pioneering team as a Prognostics & Health Monitoring Engineer and play a critical role in ensuring the reliability and longevity of cutting-edge autonomous vehicle technology. You will be instrumental in developing sophisticated algorithms that predict potential issues before they arise, directly impacting the safety, efficiency, and operational excellence of an advanced fleet. Your expertise will empower maintenance teams to proactively address component health, optimize schedules, and maintain the highest standards of performance for a revolutionary product.

What You'll Do:

Define technical requirements for new health monitors by identifying problems, necessary telemetry data, and validation data.\nDesign and train data-driven and/or physics-based prognostic models to detect faults and estimate the Remaining Useful Life (RUL) of critical hardware components.\nDevelop offline diagnostic algorithms to detect anomalies, wear-and-tear patterns, and early fault indicators using batch telemetry, sensor logs, and historical maintenance data.\nClean, filter, and extract relevant features from large volumes of time-series sensor data (e.g., vibration, temperature, voltage, pressure).\nPerform large-scale ETL and data manipulation using powerful data processing frameworks on cloud-based platforms.\nEngineer, package, and deploy developed models using production-grade pipelines.\nDesign and implement fleet result dashboards to visualize monitor output.\nDevelop a robust alerting strategy, secure stakeholder confirmation, and configure alert delivery.\nRigorously back-test prognostic models against historical failure data to ensure high accuracy, low false-positive rates, and reliability.\nProvide clear technical documentation of model architectures, deployment procedures, and codebases to ensure a smooth handover at the end of the contract.

Must-Have Qualifications:

BS Degree in Mechanical Engineering, Electrical Engineering, Data Science, or a related field.\n3+ years of experience specifically focused on Prognostics and Health Management (PHM), predictive maintenance, or reliability engineering.\nExpertise in Python and powerful data processing frameworks for large-scale data manipulation, ETL, and feature engineering.\nHands-on experience working with cloud-based data platforms for development, deployment, and dashboard creation.\nHands-on experience deploying and scheduling analytical workflows using workflow orchestration tools.\nProficiency with version control using modern systems.\nDeep understanding of anomaly detection, time-series forecasting, survival analysis, and machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).\nExperience applying signal processing techniques (e.g., FFT, wavelet transforms, filtering) to raw sensor data.\nFamiliarity with deploying batch-processing or offline analytical scripts using containerization, cloud data pipelines, or similar infrastructure.\nSolid understanding of hardware mechanics, fatigue, degradation models, or failure modes (FMEA/FMECA).

Nice-to-Have Qualifications:

Prior experience in the Automotive/Aerospace sector.\nExperience working with large-scale data storage and querying (SQL, distributed computing platforms, etc.).\nMaster's or Ph.D. in Mechanical Engineering, Electrical Engineering, Data Science, or a related field. 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