Staff Machine Learning Engineer

About the position

Root is seeking a Staff Machine Learning Engineer to serve as the expert in machine learning orchestration and deployment, while upskilling others throughout the org. This position will partner with teams across the organization to identify and execute on opportunities for greater agility and automation in both research and production systems. This individual will be a central thought leader, while also contributing hands-on within areas most pivotal to company strategy. This position will embed within our data science organization, though will forge deep connective relationships with counterparts in platform engineering, information security, and cloud management. Over a multi-year timeframe, this role will have the opportunity to shape and scale Root’s formalized ML ops strategy. Root is a “work where it works best” company. Meaning we will support you working in whatever location that works best for you across the US. We will continue to have our headquarters in Columbus and offices in other locations to give more flexibility and more choice about how we live and work.

Responsibilities

• 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.

Benefits

• Salary Range: $200,000 - $250,000 (Bonus and LTI eligible)