Machine Learning Engineer, Predictive Maintenance at MAINTAINX INC

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

13 Feb, 26

Salary

0.0

Posted On

15 Nov, 25

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Data Science, Statistical Modeling, Time-Series Modeling, Feature Engineering, Python, PyTorch, Tensorflow, Signal Processing, Vibration Analysis, Condition Monitoring, Fault Detection, Exploratory Data Analysis, Algorithm Evaluation, Collaboration, Documentation

Industry

Software Development

Description

MaintainX is the world’s leading mobile-first Asset and Work Intelligence platform for industrial and frontline environments. We’re a modern, IoT-enabled, cloud-based solution that powers maintenance, safety, and operations on physical equipment and facilities. We help 12,000+ organizations—including Duracell, Univar Solutions, Titan America, McDonald’s, Brenntag, Cintas, Xylem, and Shell—achieve operational excellence and reliability at scale. Following our $150 million Series D led by Bain Capital Ventures, Bessemer Ventures, August Capital, Amity Ventures, and Ridge Ventures, MaintainX has raised a total of $254 million, valuing the company at $2.5 billion. As we enter our next phase of growth, we’re investing deeply in AI/ML, LLMs, and Industrial IoT to transform how frontline teams operate—predicting failures before they happen, automating workflows, and embedding intelligence into every asset and procedure. What you’ll do: Develop and train machine learning models for fault detection and classification based on time-series sensor data; including vibration, temperature, pressure, flow etc. Perform exploratory data analysis (EDA) on vibration, OT and time-series data to uncover insights and identify patterns indicative of faults or anomalies. Experiment with and evaluate various algorithms, including time-series modeling, signal processing, and statistical methods, to optimize model performance. Collaborate with domain experts to validate findings and ensure alignment with real-world applications. Document workflows, experiments, and methodologies to ensure reproducibility and knowledge sharing across the team. On-call duties About you: Strong foundational knowledge in machine learning, data science, and statistical modeling. Familiarity with time-series modeling techniques and feature engineering. Experience in deploying machine learning models on real-world use cases and continuously improving the model performance with feedback. 3+ years of proven programming skills using standard ML tools such as Python, PyTorch, Tensorflow etc. Master’s or Ph.D. in Computer Science, Data Science, Mechanical Engineering, Electrical Engineering, or a related field with a focus on condition monitoring or machine learning applications. Bonus skills: Hands-on experience developing models for OT and vibration analysis, condition monitoring, and fault detection or classification. Familiarity with signal processing techniques (e.g., Fourier transforms, wavelet analysis) and their application to OT and vibration data. What’s in it for you: Competitive salary and meaningful equity opportunities. Healthcare, dental, and vision coverage. 401(k) / RRSP enrollment program. Take what you need PTO. A high impact Culture: You’ll work with Smart, Humble Optimists across the globe. Meritocratic environment where ideas and outcomes are publicly celebrated. About us: We exist to make the lives of frontline and maintenance teams easier by building software that meets their real-world needs. Our product transforms how 80% of the global workforce—those who don’t sit behind a desk—manage their operations, assets, and teams. MaintainX is committed to creating a diverse environment. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.

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Responsibilities

Develop and train machine learning models for fault detection and classification based on time-series sensor data. Collaborate with domain experts to validate findings and ensure alignment with real-world applications.