Interested in this AI/ML Engineer role at Root Insurance Company?
Apply Now →Skills & Technologies
About This Role
At Root, we’re on a mission to improve the lives of our customers by offering better insurance solutions. We challenge ourselves to think differently in order to reimagine insurance to make it smarter, more equitable, and a better experience for all.
We strive to “unbreak” the archaic insurance industry by using data and technology in innovative new ways. We believe we must be steadfast in our commitments to research, experimentation, and disciplined data\-driven decision making in order to build products our customers love.
The Opportunity
We believe that a disruptive insurance company must have a principled quantitative framework at its foundation. At Root, we are committed to the rigorous development and effective deployment of modern statistical machine learning methods to problems in the insurance industry.
Root is seeking a Lead Machine Learning Engineer I to help build the systems and workflows that power our customer lifetime value modeling ecosystem.
In this role, you will partner closely with data scientists, engineers, and business teams to build scalable machine learning systems that support high\-impact decision\-making across Marketing, Finance, Product, and Customer Experience. You will help accelerate the path from experimentation to production while improving the reliability and operational maturity of Root’s ML ecosystem.
This role focuses on building the infrastructure, tooling, and operational patterns that allow machine learning systems to scale reliably in production. You will help shape the foundations that enable statistical models, simulations, and forecasts to drive measurable business impact across the organization.
The ideal candidate is a machine learning engineer who enjoys building high\-leverage systems, improving how technical teams work, and enabling machine learning to operate reliably at scale.
Root is a “work where it works best” company, meaning we will support you working in whatever location works best for you across the U.S.
Salary Range: $164,000 \- $205,000 (Eligible for Competitive Bonus \& Equity Offering)
How You Will Make an Impact
- Build and improve the systems that power customer lifetime value modeling, from development and deployment through monitoring and production support.
- Partner with data scientists to productionize statistical models, simulations, and forecasting workflows that support decision\-making across the business.
- Accelerate the path from research to production through scalable infrastructure, reliable workflows, and reusable tooling.
- Improve the ML development experience by building better operational patterns and advancing production\-ready ML practices.
- Develop tools and services that help stakeholders evaluate model performance, understand business impact, and trust model outputs in production.
- Collaborate with technical and business partners to solve high\-value problems and improve the reliability and scalability of ML systems.
- Share best practices through mentorship, documentation, and clear communication around technical decisions, tradeoffs, and operational considerations.
What You Will Need to Succeed
- BS in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- 5\+ years of experience designing, building, deploying, and maintaining machine learning systems and ML model pipelines in partnership with data scientists.
- Strong Python and software engineering fundamentals, with the ability to build maintainable ML systems and production\-quality code.
- Experience building and operating production ML systems, including deployment, monitoring, debugging, and workflow orchestration.
- Ability to design reproducible systems with clear lineage, versioning, and operational visibility across complex ML workflows.
- Comfort working in ML systems with interconnected components, simulation\-driven logic, and embedded business rules.
- Strong judgment around model evaluation, code quality, system reliability, and maintainable engineering tradeoffs.
- Experience with cloud\-based ML infrastructure and data platforms such as AWS, GCP, or Azure.
- Experience with infrastructure as code, such as Terraform.
- Clear communication skills and the ability to explain technical tradeoffs to both technical and non\-technical audiences.
Nice to Have
- MS or PhD in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field.
- Familiarity with customer lifetime value forecasting, simulation workflows, or Forecast vs. Actual analysis.
- Experience with insurance or regulated financial products.
- Exposure to ML and data tooling, orchestrators, and platforms such as MLflow, Airflow, Dagster, Snowflake, Databricks, dbt, and Spark
- Experience building shared ML infrastructure, developer tooling, or reusable systems that improve data science productivity.
As part of Root's interview process, we kindly ask that all candidates be on camera for virtual interviews. This helps us create a more personal and engaging experience for both you and our interviewers. Being on camera is a standard requirement for our process and part of how we assess fit and communication style, so we do require it to move forward with any applicant's candidacy. If you have any concerns, feel free to let us know once you are contacted. We’re happy to talk it through.
Consistent with the Americans with Disabilities Act (ADA) and the Civil Rights Act of 1964, it is the policy of Root to provide reasonable accommodation when requested by a qualified applicant or candidate with a disability, unless such accommodation would cause an undue hardship for Root. The policy regarding requests for reasonable accommodation applies to all aspects of the hiring process. If reasonable accommodation is needed, please contact [email protected].
Salary Context
This $164K-$205K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Root Insurance Company, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. Disclosed range: $164K to $205K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Root Insurance Company AI Hiring
Root Insurance Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US. Compensation range: $205K - $205K.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.