Senior Applied Machine Learning Engineer (Asset Intelligence)

Join MaintainX as a Senior Applied Machine Learning Engineer, where you'll lead 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.

Lead the technical direction and architecture of Predictive Maintenance and Asset Intelligence initiatives, combining deep ML expertise with strong software development skills.

About MaintainX

Mentor a growing team of ML and data developers, instilling best practices for experimentation, evaluation, and model lifecycle management, and driving the roadmap for AI-enabled maintenance intelligence.

Proven experience deploying production ML systems—not just prototypes—at scale

Background in time-series modeling for predictive maintenance and anomaly detection

Experience with OCR for extracting structured data from documents

Responsibilities

• Architect end-to-end ML systems, from data ingestion and feature development to model training, deployment, and monitoring, ensuring ML infrastructure meets production standards.
• Demonstrated ability to lead architectural decisions, mentor developers, and collaborate across product, data, and platform teams

Requirements

• Expertise in Python, and strong familiarity with PyTorch, TensorFlow, and cloud ML stacks (AWS, Databricks, or similar)
• 7+ years of experience in Machine Learning, Data Science, or Applied AI
• Knowledge of MLOps tooling (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker)
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field preferred
• Strong background in LLMs, time-series modeling, and anomaly detection for real-world data
• 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

Preferred

• Familiarity with Industrial IoT systems (sensors, telemetry, edge computing)