Principal ML Engineer

Prodege, LLC is a cutting-edge marketing and consumer insights platform that is seeking a Principal ML Engineer to lead the architecture and design of machine learning within their Performance Marketing domain. This role involves defining the ML vision, guiding model and data architecture, and mentoring a growing team of engineers and data scientists while fostering an AI-first engineering mindset.

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

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
• 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