Senior Applied Machine Learning Engineer - Asset Intelligence

MaintainX•San Francisco, CA

37d

About The Position

MaintainX is the world's leading mobile-first Asset and Work Intelligence platform for industrial and frontline environments. We're a modern, IoT-enabled, cloud-based solution that powers maintenance, safety, and operations on physical equipment and facilities. We help 12,000+ organizations-including Duracell, Univar Solutions, Titan America, McDonald's, Brenntag, Cintas, Xylem, and Shell-achieve operational excellence and reliability at scale. Following our $150 million Series D led by Bain Capital Ventures, Bessemer Ventures, August Capital, Amity Ventures, and Ridge Ventures, MaintainX has raised a total of $254 million, valuing the company at $2.5 billion. 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 Engineer to guide the technical direction and architecture of our Predictive Maintenance and Asset Intelligence initiatives. You'll combine deep ML expertise with strong software engineering and leadership skills-mentoring engineers, 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.

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.

Nice To Haves

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.

Responsibilities

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.