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Posted May 28, 2026

Senior Applied Machine Learning Engineer, Asset Intelligence

Job Description: • 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 engineering to model training, deployment, and monitoring. • Mentor a growing team of ML and data engineers, instilling best practices for experimentation, evaluation, and model lifecycle management. • Partner with product and engineering 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. Requirements: • 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 engineers, 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. Benefits: • Competitive salary and meaningful equity opportunities. • Healthcare, dental, and vision coverage. • 401(k) / RRSP enrollment program. • Take what you need PTO. • A high impact Culture: • You’ll work with Smart, Humble Optimists across the globe. • Meritocratic environment where ideas and outcomes are publicly celebrated.