Note: The job is a remote job and is open to candidates in USA. Prodege is a cutting-edge marketing and consumer insights platform that empowers brands and marketers through innovative technology. They are seeking a Principal Machine Learning Engineer to lead the development of production ML systems that directly influence business outcomes in a high-scale AdTech/MarTech environment.
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
Skills
- 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
- Strong hands-on background in recommendation
- Strong hands-on background in rewards / incentives
- Strong hands-on background in ROAS / LTV prediction
- Strong hands-on background in personalization / optimization systems
- Proven experience designing, shipping, and operating production ML systems end to end
- Strong understanding of offline / online ML architecture
- Strong understanding of feature engineering and feature platforms
- Strong understanding of model serving patterns
- Strong understanding of experimentation frameworks for ML systems
- Strong understanding of A/B testing and measurement design
- Strong understanding of 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
- 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
Benefits
- Medical, dental, vision, STD, LTD and basic life insurance
- Flexible PTO
- Paid sick leave prorated based on hire date
- Eight paid holidays throughout the calendar year
Company Overview