Senior Applied Machine Learning Developer (Asset Intelligence)

Join MaintainX as a Senior Applied Machine Learning Developer, where you'll lead the technical direction and architecture of our Predictive Maintenance and Asset Intelligence initiatives. You'll combine your 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 is pivotal in 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.
• Architect end-to-end ML systems, from data ingestion and feature development to model training, deployment, and monitoring, ensuring ML infrastructure meets production standards.
• 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.

• 7+ years of experience in Machine Learning, Data Science, or Applied AI
• Proven experience deploying production ML systems—not just prototypes—at scale
• Expertise in Python, and strong familiarity with PyTorch, TensorFlow, and cloud ML stacks (AWS, Databricks, or similar)
• Demonstrated ability to lead architectural decisions, mentor developers, and collaborate across product, data, and platform teams
• Strong background in LLMs, time-series modeling, and anomaly detection for real-world data
• Knowledge of MLOps tooling (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker)
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field preferred
• Background in time-series modeling for predictive maintenance and anomaly detection
• Experience with OCR for extracting structured data from documents
• Experience applying reinforcement learning or agentic architectures for decision-making and control systems
• Familiarity with Industrial IoT systems (sensors, telemetry, edge computing)
• Contributions to open-source ML frameworks or research in reliability, explainability, or digital twins