MaintainX
Senior Applied Scientist, ML Predictive Maintenance (Asset Intelligence)
Part remote, part office, you need to live within commuting distance of a named location.
Hybrid · Canada
Employer listed it 7 days ago · Added 4 days ago
First listed 7 days ago and still open.
Salary
$123,000–$180,000
Location
Hybrid · Canada
Timezone
US East
Contract
Full-time
Experience
Senior
Category
Software
Published by the employer
Remote flexibility
Hybrid
This role is only partly remote, the employer expects time in the office around Canada, Hybrid, so you need to live within commuting distance.
What the employer says
- Source listing states candidate location: "Canada, Hybrid"
- Listing mentions "Hybrid"
What Nomaders makes of it
- Not suitable if you plan to move between countries
The quotes above are the employer's own words; the reading is ours. Always check the original listing and employment terms before working from another country.
About the role
MaintainX is a leading mobile-first work execution platform for industrial and frontline teams. More than 13,000 customers , including Duracell, McDonald's, Shell, DHL and Volvo, use MaintainX to cut unplanned downtime and run better operations, across 13.9 million managed assets and 79.5 million completed work orders.
In August 2026 MaintainX became part of Autodesk, joining Autodesk Operations Solutions , the organization unifying Autodesk's operations platform alongside Tandem, FlexSim and Fusion Operations. Autodesk's strategy is to converge design, make and operate into one continuous lifecycle: design an asset, build it, run it, then feed what you learn running it back into the next design. Autodesk had design and make. Operate is the phase that tells you what actually happened, and it is ours.
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:
Design, develop and optimize machine learning models for fault detection and classification end-to-end e.g. data and training modeling choices to evaluation strategies and production constraints.
Perform EDA on vibration, OT and time-series data to uncover insights and identify patterns indicative of faults or anomalies.
Conduct experiments and evaluation of various algorithms on time-series modeling, signal processing, and statistical methods, to optimize model performance.
Partner with PMs in product feature discovery and roadmap prioritization through validating product hypotheses, designing success metrics and quantifying end user impact
Collaborate with domain experts to validate findings and ensure alignment with real-world applications.
Engage with your community of peers to challenge the status quo, improve our shared ways of working, and influence overall architecture decisions, continuing to foster our culture of Applied Science excellence
On-call duties
About you:
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.
5+ years of proven programming skills using standard ML tools such as Python, PyTorch, Tensorflow etc.
Strong foundational knowledge in machine learning, data science, and statistics
Familiarity with time-series modeling techniques and feature engineering.
Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.
An expert level of English, both spoken and written, is required, as the individual will need to lead platform engineering managers across multiple teams, present technical priorities to executive stakeholders, and align with engineering leaders outside Québec on a daily basis.
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.
Our mission is to keep the physical world running. Factories, fleets, hospitals and campuses stay up because the people who maintain them have tools worth using. That is what we build.
Compensation and benefits. Base pay is one part of the package. Depending on the role, compensation may also include commission, an annual bonus and equity. Benefits differ by country. For roles in the United States, Autodesk’s benefits are described at benefits.autodesk.com . For roles in Canada and other countries, the plan differs on health coverage, retirement and leave, and your recruiter will walk you through it.
Belonging. We take pride in a culture where everyone can thrive. More at autodesk.com/company/global-belonging . More on where this is going: Autodesk CEO Andrew Anagnost on building the future of connected operations, and AOS SVP Stephen Hooper on welcoming MaintainX to Autodesk.
Requirements
- ·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.
- ·5+ years of proven programming skills using standard ML tools such as Python, PyTorch, Tensorflow etc.
- ·Strong foundational knowledge in machine learning, data science, and statistics
- ·Familiarity with time-series modeling techniques and feature engineering.
- ·Ability to deliver production-grade code that is well-tested, maintainable, and evaluated through rigorous experimentation.
Benefits
No benefits package published with this listing. Ask about it at first interview.
How to apply
- 1Check the flexibility label above, hybrid, matches where you plan to live and work.
- 2Tailor your CV to the role at MaintainX, mentioning your remote working experience and working hours (US East).
- 3Apply directly on the employer's careers page using the button below. Nomaders never handles your application.
Found 5d ago. Last checked today. Always confirm the details on the original posting, salary and location can change after publication.
Listing sourced from Company boards.
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