Senior Applied Machine Learning Engineer - Asset Intelligence at MaintainX

Job description

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 adsknews.autodesk.com, 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. The Role We are seeking a highly skilled and motivated Senior Applied Machine Learning Developer to guide the technical direction and architecture of our Predictive Maintenance and Asset Intelligence initiatives. You’ll combine deep ML expertise with strong software development and leadership skills—mentoring developers, scaling systems, and driving the roadmap for AI-enabled maintenance intelligence across thousands of industrial sites. This role sits at the intersection of ML architecture, IoT data systems, and product impact, shaping the foundation for MaintainX’s predictive and generative AI strategy. What you’ll do: - Lead technical direction for predictive maintenance, anomaly detection, and LLM-powered intelligence across MaintainX products. - Architect end-to-end ML systems—from data ingestion and feature development to model training, deployment, and monitoring. - Mentor a growing team of ML and data developers, instilling best practices for experimentation, evaluation, and model lifecycle management. - Partner with product and software development leaders to align AI roadmap with customer needs and business goals. - Design reliable data and feedback loops that connect customer telemetry and operator feedback to model retraining. - Drive performance optimization through techniques like quantization, distillation, and scalable inference serving. - Work with LLM frameworks (LangChain, LlamaIndex, Hugging Face) to build reasoning systems and agentic workflows for asset and work intelligence. - Ensure ML infrastructure meets production standards for latency, reliability, explainability, and security. About you: - 7+ years of experience in Machine Learning, Data Science, or Applied AI. - Expertise in Python, and strong familiarity with PyTorch, TensorFlow, and cloud ML stacks (AWS, Databricks, or similar). - Proven experience deploying production ML systems—not just prototypes—at scale. - Strong background in LLMs, time-series modeling, and anomaly detection for real-world data. - Demonstrated ability to lead architectural decisions, mentor developers, and collaborate across product, data, and platform teams. - Knowledge of MLOps tooling (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker). - Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field preferred. Bonus skills: - Experience with OCR for extracting structured data from documents. - Background in time-series modeling for predictive maintenance and anomaly detection. - Familiarity with Industrial IoT systems (sensors, telemetry, edge computing). - Experience applying reinforcement learning or agentic architectures for decision-making and control systems. - Contributions to open-source ML frameworks or research in reliability, explainability, or digital twins. 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 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 autodesk.com. 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

Must-haves

We are seeking a highly skilled and motivated Senior Applied Machine Learning Developer to guide the technical direction and architecture of our Predictive Maintenance and Asset Intelligence initiatives.
This role sits at the intersection of ML architecture, IoT data systems, and product impact, shaping the foundation for MaintainX’s predictive and generative AI strategy.
Lead technical direction for predictive maintenance, anomaly detection, and LLM-powered intelligence across MaintainX products.
Architect end-to-end ML systems—from data ingestion and feature development to model training, deployment, and monitoring.
Mentor a growing team of ML and data developers, instilling best practices for experimentation, evaluation, and model lifecycle management.
Partner with product and software development leaders to align AI roadmap with customer needs and business goals.
Design reliable data and feedback loops that connect customer telemetry and operator feedback to model retraining.
Drive performance optimization through techniques like quantization, distillation, and scalable inference serving.
Work with LLM frameworks (LangChain, LlamaIndex, Hugging Face) to build reasoning systems and agentic workflows for asset and work intelligence.
Ensure ML infrastructure meets production standards for latency, reliability, explainability, and security.
7+ years of experience in Machine Learning, Data Science, or Applied AI.
Expertise in Python, and strong familiarity with PyTorch, TensorFlow, and cloud ML stacks (AWS, Databricks, or similar).
Proven experience deploying production ML systems—not just prototypes—at scale.
Strong background in LLMs, time-series modeling, and anomaly detection for real-world data.
Demonstrated ability to lead architectural decisions, mentor developers, and collaborate across product, data, and platform teams.
Knowledge of MLOps tooling (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker).
Experience with OCR for extracting structured data from documents.
Background in time-series modeling for predictive maintenance and anomaly detection.
Familiarity with Industrial IoT systems (sensors, telemetry, edge computing).
Experience applying reinforcement learning or agentic architectures for decision-making and control systems.
Contributions to open-source ML frameworks or research in reliability, explainability, or digital twins.
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.

Nice-to-haves

Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field preferred.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimlmachine learningllmpythonawsdockerkubernetes

Platform: ashby
Effort: quick