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MaintainX is seeking a Senior Applied Machine Learning Developer to lead technical direction and architecture for Predictive Maintenance and Asset Intelligence initiatives. The company is the world's leading AI-powered maintenance and asset management platform serving 13,000+ customers including Duracell, Shell, Cintas, and Brenntag, with $254M in total funding and a $2.5B valuation.
In this role, you will lead technical direction for predictive maintenance, anomaly detection, and LLM-powered intelligence across MaintainX products. You'll architect end-to-end ML systems from data ingestion and feature development through model training, deployment, and monitoring. A key responsibility is mentoring a growing team of ML and data developers, instilling best practices for experimentation, evaluation, and model lifecycle management.
You'll partner with product and software development leaders to align the AI roadmap with customer needs and business goals. You'll design reliable data and feedback loops connecting customer telemetry and operator feedback to model retraining, and drive performance optimization through techniques like quantization, distillation, and scalable inference serving. The role involves working with LLM frameworks (LangChain, LlamaIndex, Hugging Face) to build reasoning systems and agentic workflows for asset and work intelligence, while ensuring ML infrastructure meets production standards for latency, reliability, explainability, and security.
Required qualifications include 7+ years in Machine Learning, Data Science, or Applied AI; expertise in Python with strong familiarity with PyTorch, TensorFlow, and cloud ML stacks (AWS, Databricks); proven experience deploying production ML systems at scale; strong background in LLMs, time-series modeling, and anomaly detection; demonstrated ability to lead architectural decisions and mentor developers; and knowledge of MLOps tooling (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker). An advanced degree in Computer Science, Machine Learning, or related field is preferred.
Bonus skills include OCR experience, time-series modeling for predictive maintenance, Industrial IoT systems knowledge, reinforcement learning or agentic architectures experience, and contributions to open-source ML frameworks or research in reliability and explainability.
