Machine Learning Engineer, Predictive Maintenance at Jobgether

, California, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Feb, 26

Salary

0.0

Posted On

17 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Predictive Maintenance, Time-Series Data, Signal Processing, Data Analysis, Python, PyTorch, TensorFlow, Statistical Modeling, Feature Engineering, Predictive Analytics, Model Deployment, Collaboration, Communication, Problem-Solving

Industry

Internet Marketplace Platforms

Description

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Machine Learning Engineer, Predictive Maintenance in California (USA). As a Machine Learning Engineer focused on predictive maintenance, you will develop cutting-edge models that analyze industrial IoT and sensor data to detect anomalies and forecast equipment failures. You will collaborate with domain experts and cross-functional teams to translate complex operational data into actionable insights, driving reliability and efficiency across large-scale industrial operations. This role offers the opportunity to work with high-frequency time-series data, apply advanced signal processing techniques, and implement ML models in real-world, high-impact environments. You will contribute to both research and production-ready solutions while documenting methodologies to ensure reproducibility and knowledge sharing. Ideal candidates thrive in innovative, fast-paced settings and are passionate about leveraging AI to transform frontline operations. Accountabilities · Develop and train machine learning models for fault detection and predictive maintenance using time-series sensor data such as vibration, temperature, and pressure. · Conduct exploratory data analysis (EDA) to uncover patterns, anomalies, and insights in OT and industrial data. · Experiment with and optimize algorithms including time-series modeling, signal processing, and statistical methods. · Collaborate closely with domain experts and engineering teams to validate models and align outcomes with operational requirements. · Deploy models to production environments and monitor performance, iteratively improving results with real-world feedback. · Document workflows, experiments, and methodologies to ensure reproducibility and facilitate knowledge sharing. · Participate in on-call rotations to address urgent system or model issues. · Master’s or Ph.D. in Computer Science, Data Science, Mechanical Engineering, Electrical Engineering, or a related field. · 3+ years of experience in programming with ML frameworks such as Python, PyTorch, or TensorFlow. · Strong foundation in machine learning, statistical modeling, and data science principles. · Familiarity with time-series modeling techniques, feature engineering, and predictive analytics. · Experience deploying ML models in production and continuously monitoring and improving their performance. · Bonus: Hands-on experience with condition monitoring, vibration analysis, signal processing techniques (e.g., Fourier transforms, wavelet analysis), or fault classification models. · Strong collaboration, communication, and problem-solving skills. · Competitive salary with meaningful equity opportunities. · Comprehensive healthcare, dental, and vision coverage. · 401(k) / RRSP enrollment program. · Flexible PTO to take what you need. · High-impact culture with meritocratic recognition and global collaboration. · Opportunities to work with cutting-edge industrial IoT and AI technologies in real-world applications. Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching. When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly. 🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements. 📊 It compares your profile to the job’s core requirements and past success factors to determine your match score. 🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role. 🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed. The process is transparent, skills-based, and free of bias—focusing solely on your fit for the role. Once the shortlist is completed, it is shared directly with the company. The final decision and next steps (such as interviews or additional assessments) are made by their internal hiring team. Thank you for your interest! #LI-CL1

Responsibilities

Develop and train machine learning models for fault detection and predictive maintenance using time-series sensor data. Collaborate with domain experts and engineering teams to validate models and deploy them in production environments.