Pricing Platform ML Architect (Staff)

joinroot; United States; Remote; USD 189,000 - 265,000; Full time

Benefits offered by this job

Competitive bonus

Equity

Job summary

Root is seeking a Staff Machine Learning Engineer to lead the pricing ML platform, owning long‑term architecture across data, features, models, and pricing. You will work with researchers to move ideas from experimentation to production, and mentor engineers to raise the bar on reliability and observability.

You will design scalable ML infrastructure, including feature pipelines, model training orchestration, and model serving, while exploring LLM-driven automation and ensuring reproducibility

Qualifications

• 10+ years of software engineering with ML platform delivery.
• Ownership of production ML infrastructure and related systems.
• Strong system design for reliable, scalable data processing.
• Experience ensuring reproducibility, lineage, and training-serving consistency.
• Know-how of ML lifecycle from data readiness to deployment.

Responsibilities

• Define long-term technical roadmap to accelerate pricing innovation.
• Collaborate with researchers to translate requirements into platform capabilities.
• Define architecture and versioned contracts across data, features, models, and pricing.
• Automate end-to-end ML workflows, exploring LLM-enabled automation.
• Mentor engineers and set standards for reliability and observability.
• Drive cross-team architectural design and technical direction.

Skills

10+ years experience

ML platform design

Distributed systems

Python

Research collaboration

Architecture ownership

Model training orchestration

LLM tooling

Tools

Feature pipelines

Feature stores

Model serving

Data tooling

Job description

Root is seeking a Staff Machine Learning Engineer to lead the pricing ML platform, owning long‑term architecture across data, features, models, and pricing. You will work with researchers to move ideas from experimentation to production, and mentor engineers to raise the bar on reliability and observability.

You will design scalable ML infrastructure, including feature pipelines, model training orchestration, and model serving, while exploring LLM-driven automation and ensuring reproducibility