Principal ML Engineer

Prodege•El Segundo, CA

10d•$300,000 - $375,000

About The Position

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. Bolstered by a major investment from Blackstone, Prodege is focused on growth and innovation. The company aims to empower partners to gather meaningful insights and market effectively. Prodege emphasizes creating rewarding moments for its partners, consumers, and team members. This role is for a Principal Machine Learning Engineer who will shape the future of machine learning within Prodege’s Performance Marketing business. It's a high-impact position for an individual who wants to own more than just models, focusing on building and evolving production ML systems that drive outcomes in ranking, rewards, ROAS/LTV prediction, offer optimization, experimentation, and decisioning. The work will directly influence revenue, margin, user value, and marketplace efficiency in a fast-paced AdTech/MarTech environment. This is a deeply hands-on principal role, requiring leadership through building, shipping, and operating production ML systems, not just focusing on architecture or strategy. The individual will own the ML stack end-to-end, from problem framing and feature strategy to model development, experimentation, deployment, observability, and lifecycle optimization. The role involves building production ML systems for a business serving over 120 million registered users, which has delivered over $2 billion in lifetime rewards. This is supported by a data platform handling 50 million events per day, 500 million records of daily pipeline throughput, a 100TB Iceberg lake, and 50 Kafka topics, encompassing both batch and real-time workflows. The ideal candidate enjoys building real-world ML systems, working closely with the business, and contributing to an AI-first engineering model.

Requirements

8+ years of experience in software engineering, machine learning engineering, MLOps, or related technical fields
5+ years building, deploying, and supporting production ML systems at scale
Strong experience in AdTech, MarTech, Growth, Performance Marketing, or adjacent domains
Strong hands-on background in: ranking, recommendation, rewards / incentives, ROAS / LTV prediction, personalization / optimization systems
Proven experience designing, shipping, and operating production ML systems end to end
Strong understanding of: offline / online ML architecture, feature engineering and feature platforms, model serving patterns, experimentation frameworks for ML systems, A/B testing and measurement design, MLOps, retraining, monitoring, and governance
Experience partnering closely with Data Engineering / BI / Analytics teams to create clean, scalable, and trustworthy data foundations for ML
Strong system design skills with sound judgment across performance, reliability, scalability, and cost
Ability to guide teams toward an AI-first way of working, while maintaining strong validation and engineering discipline
Strong technical leadership and mentoring capability, with the ability to influence across teams without direct authority
Comfort operating in ambiguity and still driving systems into production

Nice To Haves

Experience with counterfactual reasoning, causal inference, or uplift modeling
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
Master’s degree or PhD in AI, Machine Learning, or a quantitative field
Familiarity with modern AI-assisted / AI-first development practices across engineering and data science teams

Responsibilities

Lead the design, build, and evolution of production ML algorithms and systems that drive real business outcomes
Personally drive critical implementations, proving out new approaches in production before scaling them across the team
Architect and ship scalable ML systems across offline training, online inference, feature pipelines, feedback loops, and model monitoring
Build and evolve solutions across: ranking and recommendation, rewards optimization, ROAS / LTV prediction, campaign and offer optimization, experimentation and decisioning systems
Establish robust experimentation and measurement frameworks, including offline evaluation, A/B testing, KPI design, and post-launch validation
Make key decisions on MLOps, tooling, infrastructure, serving patterns, observability, and platform architecture
Partner closely with Data Engineering, BI, Product, Engineering, and business teams to create reliable data foundations and connect ML work to business priorities
Drive an AI-first mindset by using AI to accelerate research, prototyping, feature engineering, experiment analysis, debugging, documentation, and developer productivity
Mentor ML engineers and data scientists by leading through direct contribution and raising the bar on model quality, technical judgment, and engineering rigor