Staff Machine Learning Engineer

Prodege•El Segundo, CA

About The Position

This is a role for someone who wants to solve hard machine learning problems by building production systems that matter. We're looking for a Staff Machine Learning Engineer who loves shipping production ML systems, owning complex technical problems end-to-end, and partnering closely with Product, Data Engineering, and the business to deliver measurable outcomes. This is a deeply hands-on individual contributor role. You'll spend the majority of your time building, deploying, experimenting with, and improving production machine learning systems, not managing people or operating primarily at the architectural strategy level. If you enjoy taking ownership of difficult ML problems, iterating quickly through experimentation, and seeing your work directly improve revenue, marketplace efficiency, and customer experience, this role is for you. You'll build production ML systems for a business serving 120M+ registered users that has delivered $2B+ in lifetime rewards, powered by a data platform with 50M daily events, 500M daily pipeline records, a 100TB Iceberg lake, and 50 Kafka topics supporting both batch and real-time workflows. Prodege: A cutting-edge marketing and consumer insights platform, Prodege has charted a course of innovation in the evolving technology landscape by helping leading brands, marketers, and agencies uncover the answers to their business questions, acquire new customers, increase revenue, and drive brand loyalty & product adoption. Bolstered by a major investment by Blackstone in Q1 2026, Prodege looks forward to more growth and innovation to empower our partners to gather meaningful, rich insights and better market to their target audiences. As an organization, we go the extra mile to "Create Rewarding Moments" every day for our partners, consumers, and team. Come join us today!

Requirements

6+ years of experience in Machine Learning Engineering, Software Engineering, MLOps, or related technical fields.
3+ years building, deploying, and operating production machine learning systems.
Strong experience building production recommendation, ranking, personalization, optimization, or prediction systems.
Experience working in AdTech, MarTech, Growth, Consumer Products, Marketplace platforms, or adjacent domains.
Strong understanding of: Feature engineering, Offline and online inference, Experimentation and A/B testing, Model serving, Monitoring and retraining, MLOps best practices
Experience partnering closely with Product, Engineering, and Data teams to deliver measurable business outcomes.
Strong software engineering fundamentals with excellent coding skills.
Comfort operating in ambiguous environments while independently driving technical solutions.
Demonstrated ability to mentor engineers and influence technical decisions across teams.

Nice To Haves

Experience with ROAS optimization, bidding systems, rewards platforms, or monetization models.
Experience with streaming or near-real-time ML systems.
Experience with recommendation engines or personalization at scale.
Experience using feature stores or shared ML infrastructure.
Experience with causal inference, uplift modeling, or counterfactual reasoning.
Master's degree or PhD in Machine Learning, AI, Computer Science, or a quantitative discipline.
Experience using AI-assisted development tools in software engineering workflows.

Responsibilities

Design, build, and operate production machine learning systems from development through deployment
Production models supporting ranking, recommendations, personalization, rewards optimization, ROAS/LTV prediction, and offer optimization
Feature engineering, model training pipelines, online inference, experimentation, monitoring, and continuous improvement
Reliable production ML practices including testing, observability, retraining, and model health
Technical leadership through code reviews, collaboration, and mentoring less experienced engineers
Cross-functional partnerships with Product, Data Engineering, Analytics, and Business stakeholders to solve high-impact problems
Design, build, deploy, and maintain production machine learning systems.
Develop scalable ML solutions across ranking, recommendation, personalization, rewards optimization, ROAS/LTV prediction, and experimentation.
Improve feature engineering, model performance, inference latency, and operational reliability.
Design and analyze offline evaluations and A/B experiments to validate business impact.
Partner with Data Engineering to build reliable data pipelines and feature sets for ML.
Contribute to MLOps practices including deployment, monitoring, retraining, and model lifecycle management.
Review code, mentor teammates, and help raise engineering quality across the ML organization.
Leverage AI-assisted development to accelerate research, prototyping, debugging, documentation, and experimentation.