Lead ML Engineer, Performance Marketing

Root Inc.

. Lead the design and development of production ML systems powering performance marketing optimization across channels .

Posted 9/15/2026full-timeRemote • United StatesSenior💰 $164,000 - $205,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, building, and deploying machine learning systems with a focus on performance marketing optimization. Proficient in Python and experienced in cloud-based ML infrastructure, ensuring reliable and maintainable solutions across the ML lifecycle.

Highest-signal resume keywords

Machine Learning Systems DesignProduction ML DeploymentPython ProgrammingCloud-Based ML InfrastructureModel Evaluation and Monitoring

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningModel PipelinesReal-Time Model ServingWorkflow OrchestrationStatistical Model EvaluationOptimization AlgorithmsVersioning SystemsEmbedded Business LogicInfrastructure as CodeData Science Productivity

Soft Skills

Clear CommunicationMentoring

Tools & Technologies

AWSGCPAzureTerraformMLflowAirflowDatabricksSparkSnowflakeMetaflow

Certifications & Qualifications

BS in Computer ScienceMS or PhD in Computer Science

Industry Keywords

Performance MarketingAuction-Based AdvertisingAlgorithmic BiddingBid Optimization Models

Tech Stack

Tools & technologies

AirflowAWSAzureCloudGoogle Cloud PlatformPythonSparkTerraform

About the role

Key responsibilities & impact

Lead the design and development of production ML systems powering performance marketing optimization across channels
Architect ML solutions combining multiple models, optimization algorithms, and embedded business logic
Accelerate research-to-production through scalable infrastructure, reusable tooling, and improved ML development practices
Partner with data scientists to translate new models and optimization approaches into scalable production solutions
Design and operate real-time ML capabilities that perform reliably under production latency constraints
Build monitoring and observability for interconnected ML systems to enable rapid issue detection and diagnosis
Establish technical standards for maintainable, reliable ML systems and improve team development and operations
Mentor data scientists and analysts on ML systems and production engineering
Own technical direction across the ML lifecycle, from architecture and implementation through deployment and operation
Partner with data scientists, Engineering, and business stakeholders on production ML systems for performance marketing

Requirements

What you’ll need

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
Experience building and operating production ML systems, including real-time model serving, deployment, monitoring, debugging, and workflow orchestration
Ability to design reproducible systems with clear lineage, versioning, and operational visibility across complex ML workflows
Comfort working with complex ML systems combining multiple models, optimization or search algorithms, and embedded business logic
Strong judgment around model evaluation, code quality, system reliability, and maintainable engineering tradeoffs
Working knowledge of experimentation and statistical model evaluation in production ML settings
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 ability to explain technical tradeoffs to technical and non-technical audiences
On-camera participation is required for virtual interviews
Nice to have: MS or PhD in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
Nice to have: Familiarity with performance marketing systems, including auction-based advertising, algorithmic bidding, or bid optimization models
Nice to have: Exposure to ML and data tooling, orchestrators, and platforms such as MLflow, Metaflow, Airflow, Dagster, Snowflake, Databricks, dbt, and Spark
Nice to have: Experience building shared ML infrastructure, developer tooling, or reusable systems that improve data science productivity
Nice to have: Fluency using generative AI and agentic tools to accelerate end-to-end ML development

Benefits

Comp & perks

Bonus and LTI eligible
Work where it works best: support for working in whatever location works best across the U.S.
Reasonable accommodation during all aspects of the hiring process