Job Description:

We are looking for a Principal Machine Learning Engineer to shape the future of machine learning across Prodege's Performance Marketing business.

This is a high-impact role for someone who wants to own more than models. You will build and evolve the production ML systems that drive outcomes across ranking, rewards, ROAS / LTV prediction, offer optimization, experimentation, and decisioning. Your work will directly influence revenue, margin, user value, and marketplace efficiency in a fast-moving AdTech / MarTech environment.

This is a deeply hands-on principal role. We are looking for someone who leads by building, shipping, and operating production ML systems; not someone who stays only at the architecture or strategy layer. You will own the ML stack end to end, from problem framing and feature strategy through model development, experimentation, deployment, observability, and lifecycle optimization.

You will 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 events per day, 500M records of daily pipeline throughput, 100TB Iceberg lake, and 50 Kafka topics and growing across batch and real-time workflows.

If you enjoy building real-world ML systems, working close to the business, and helping a team move toward a more AI-first engineering model, this role is for you.
What You'll Own

• The architecture and delivery of offline / online ML systems, feature pipelines, inference patterns, feedback loops, and monitoring
• End-to-end ML systems spanning feature generation, training, inference, experimentation, monitoring, and lifecycle management
• Production ML algorithms and decisioning systems across ranking, rewards, ROAS / LTV, personalization, and offer optimization
• Experimentation frameworks that connect model performance to business outcomes
• Production-grade standards across MLOps, observability, retraining, governance, and reliability
• Hands-on technical leadership for the ML team through direct contribution, code reviews, and mentoring
• The evolution of ML toward a more AI-first way of working

What Makes This Role Exciting

• You will directly shape how machine learning drives revenue, margin, and user value
• You will work on analytically complex problems across ranking, rewards, ROAS, LTV, personalization, and optimization in a high-scale AdTech / MarTech environment
• You will own ML from system design through production outcome, not just model development
• You will build on top of a real production data platform operating at scale: 50M daily events, 500M daily pipeline records, 100TB Iceberg lake, and 50 Kafka topics and growing
• You will inherit a strong experimentation culture with 30+ ML experiments per month, 10 live experiments already this year, and a feature-rich data foundation with 1,000+ features, including user and item embeddings
• You will build on real business momentum - our best ranking models are already outperforming the prior models
• You will have principal-level scope to influence both the systems being built and how the broader ML organization works
• You will help push the organization toward a more AI-first engineering future

What You'll Do

• 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

The MUST Haves:

• 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 Have

• 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

Pay Transparency:

The anticipated base salary range for this position is $300,000 to $375,000. The final salary offered to a successful candidate will be dependent on several factors that may include, but are not limited to; the type and length of experience within the job, type and length of experience within the industry, the type and length of knowledge and skills for the position, education, training, etc. Prodege is a multi-state employer and final compensation within this range could be impacted by work location. Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.

Prodege 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.