Staff ML Engineer, Pricing Platform Architect

Root Inc.; United States; On-site; USD 189,000 - 265,000; Full time

Job summary

Root Inc. is seeking a Staff Machine Learning Engineer to own the long‑term architectural design of the pricing platform, including versioned contracts between data, features, models, and pricing.

You will build the next generation of Root’s ML tooling for pricing, partnering with researchers on feature stores, model training, and production deployment, while ensuring reproducibility and observability across the pricing R&D workflow.

Qualifications

• 10+ years of software engineering experience with ML platforms or complex distributed systems
• Architectural ownership of production ML infrastructure (feature pipelines, model training, model serving)
• Experience designing reliable, scalable data processing systems with clear interfaces
• Proven ability to drive reproducibility, lineage, versioning, and production correctness

Responsibilities

• Define long‑term technical roadmap for pricing ML tools and workflows
• Collaborate with researchers to define platform needs from data readiness to deployment
• Define architecture and versioned contracts between data, features, models, and production pricing
• Automate end‑to‑end workflows leveraging LLM technology for data science automation
• Write and review code in critical platform components and drive cross‑team design
• Mentor senior engineers in architecture and ML engineering best practices
• Set standards for reliability, observability, reproducibility, and correctness

Skills

ML platforms

Distributed systems

Python

System design

Research collaboration

Leadership / mentoring

Observability

Pricing platforms

LLM tooling

Communication

Job description

Root Inc. is seeking a Staff Machine Learning Engineer to own the long‑term architectural design of the pricing platform, including versioned contracts between data, features, models, and pricing.

You will build the next generation of Root’s ML tooling for pricing, partnering with researchers on feature stores, model training, and production deployment, while ensuring reproducibility and observability across the pricing R&D workflow.