Staff Machine Learning Engineer

March 1

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Root Inc.

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Root Inc. is an innovative car insurance company that utilizes mobile technology to offer personalized insurance rates based on individual driving behavior. The company operates through its app, which collects driving data via smartphone sensors, enabling it to provide more accurate and often cheaper auto insurance quotes for good drivers. Customers can manage their policies, file claims, and even earn rewards through the app, making it a convenient choice for users. Root Inc. emphasizes savings for safe drivers and provides additional features such as roadside assistance. Their services are available in various states across the U. S. , although some limitations apply depending on location.

Car Insurance • Mobile App • Technology • Automotive Telematics • Insurance

1001 - 5000 employees

Founded 2015

💳 Fintech

👥 B2C

📋 Description

• Serve as the foremost expert in the machine learning lifetime, inclusive of ML pipelines and model deployment. • Promote best practices and provide training in ML Ops, automation, and infrastructure management. • Analyze and optimize critical ML systems in applications such as marketing, risk segmentation, and lifetime value analytics. • Identify opportunities to improve efficiency, costs, and scalability in model research and deployment. • Design and build feature stores to increase the velocity of research and deploy cycles. • Build comprehensive and responsive operational tools to monitor both model fitness and infrastructure status. • Support enablement for data scientists, e.g., assisting to provision resources. • Provision and customize virtual machines to enhance data science capabilities. • Present data driven recommendations to leadership for long-term investment in ML Ops. • Evaluate, build, and integrate new tools to streamline ML development, deployment, and operations.

🎯 Requirements

• BS, MS, or PhD degree in Computer Science or related field. • 8+ years of experience designing, building, and deploying ML model pipelines at scale in production environments (e.g., as a ML engineer) • Strong ownership mentality. • Expertise in cloud-based ML pipeline infrastructure (AWS, GCP, or Azure). • Expertise in designing and deploying APIs to serve models to both internal and external endpoints. • Experience with managed ML services (e.g., Databricks, Outerbounds). • Deep expertise in Python and R, with experience in orchestration tools (e.g. airflow) and model and experiment tracking (e.g. mlflow) • Strong software engineering fundamentals. • Strong analytical and problem-solving skills with the ability to drive strategic initiatives. • Excellent communication and collaboration abilities to work with cross-functional teams. • Experience with infrastructure automation tools like Terraform. • Strong understanding of working within regulated data environments, including defining process and technical controls to be both ISO and NIST compliant.