Principal ML Engineer

Prodege • El Segundo, CA

23h

About The Position

The Principal ML Engineer will lead the architecture, design, and evolution of machine learning across the Performance Marketing domain. This role is for a senior technical leader with deep experience in AdTech / MarTech, especially in ranking, rewards, ROAS / LTV modeling, and optimization systems. The Principal ML Engineer will define the ML vision for the domain, guide model and data architecture, make key decisions on MLOps, modelling and experimentation, tooling, and scalable system design, and mentor a growing team of engineers and data scientists. The role also involves building an AI-first engineering mindset across the ML organization, using AI to improve speed, quality, insight generation, and decision-making. Prodege is a cutting-edge marketing and consumer insights platform that helps leading brands, marketers, and agencies uncover business answers, acquire new customers, increase revenue, and drive brand loyalty & product adoption. Bolstered by a major investment by Blackstone, Prodege is focused on growth and innovation to empower partners with meaningful insights and better marketing strategies. The organization aims to “Create Rewarding Moments” daily for partners, consumers, and the team.

Requirements

• Bachelor’s degree in a relevant technical field, or equivalent practical experience.
• Eight or more (8+) years of experience in software engineering, machine learning engineering, MLOps, or a related field.
• Five or more (5+) years of experience building, deploying, and supporting production machine learning systems at scale.
• Deep experience in Machine Learning engineering in AdTech, MarTech, Growth, Performance Marketing, or adjacent domains
• Strong background in: Ranking, Recommendation, rewards / incentives, ROAS / LTV prediction, personalization / optimization systems
• Proven experience designing and shipping production ML systems at scale
• Strong understanding of: feature engineering and feature stores, offline / online ML architecture, model serving patterns, experimentation frameworks for ML systems, A/B testing and measurement design, MLOps, monitoring, retraining, and model governance
• Experience with Counterfactual Reasoning, Causal Inference, or Uplift Modeling.
• Experience working closely with Data Engineering / BI / Analytics teams to ensure clean, scalable, and trustworthy data foundations for ML
• Strong system design skills with ability to make the right tradeoffs across performance, reliability, scalability, and cost
• Ability to guide teams toward an AI-first way of working, using AI as a force multiplier for model development, experimentation, and engineering productivity
• Strong judgment around where AI adds leverage and where human review, rigor, and validation remain essential
• Ability to lead technically across teams and influence architecture decisions without direct authority
• Strong mentoring and leadership skills; able to guide junior engineers and shape a strong ML engineering culture

Nice To Haves

• Master’s degree or PhD in AI, Machine Learning, or a quantitative field.
• Experience in rewards, offer ecosystems, customer value optimization, or monetization platforms
• Experience with streaming or near-real-time decisioning systems
• Experience building ML platforms or shared experimentation infrastructure
• Familiarity with modern AI-assisted / AI-first development practices across engineering and data science teams

Responsibilities

• Define the machine learning strategy and technical vision for Performance Marketing
• Lead the development of sophisticated models, from selecting loss functions and architectures to fine-tuning hyperparameters and managing deployments.
• Build "Version 0" of critical systems, writing production-quality code to prove out new modeling approaches before scaling them across the team.
• Architect and evolve ML systems across areas such as: ranking and recommendation, rewards optimization, ROAS / LTV prediction, campaign and offer optimization, experimentation and decisioning systems
• Design scalable ML architectures across offline training, online inference, feature generation, feedback loops, and model monitoring
• Partner closely with the Data team to define the right data models, data contracts, feature pipelines, training datasets, and measurement foundations needed for reliable ML systems
• Establish the right experimentation framework for ML models, including offline evaluation, online testing, A/B experimentation, KPI design, and post-launch performance measurement
• Lead by example through active code contributions and deep-dive PR reviews, ensuring high standards for model performance and system reliability.
• Make key decisions on MLOps, tooling, infrastructure, model serving, observability, and platform architecture
• Drive an AI-first mindset within the ML organization by using AI to accelerate research, prototyping, feature engineering, experimentation analysis, documentation, model debugging, and developer productivity where it makes sense
• Guide the team on how to build systems that are scalable, reliable, cost-aware, and production-ready
• Partner with Product, Engineering, Analytics, and business teams to translate commercial goals into ML roadmaps
• Mentor ML engineers and data scientists, helping raise the bar on model quality, engineering rigor, and technical judgment
• Set best practices for model validation, monitoring, retraining, drift detection, explainability, and governance

Benefits

• Prodege offers a comprehensive benefits package to US Full-time employees including medical, dental, vision, STD, LTD and basic life insurance.
• Employees receive flexible PTO, as well as paid sick leave prorated based on hire date.
• US Employees have eight paid holidays throughout the calendar year.
• Employees receive an option to purchase shares of Company stock commensurate with their position, which vests over four years.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 251-500 employees