Job Description:

Read this part first:

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.

What you'll own

• 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

What makes this role exciting

• You'll work on machine learning problems that directly impact revenue, marketplace efficiency, and customer experience.
• You'll own production systems-not just models-from experimentation through deployment and optimization.
• You'll build on top of a production platform processing 50M daily events, 500M daily pipeline records, and a 100TB Iceberg lake.
• You'll join a team with an active experimentation culture, shipping ML improvements that quickly reach production.
• You'll have significant ownership while partnering with senior technical leaders to shape the future of ML at Prodege.
• You'll work in an engineering culture embracing AI-assisted development to improve productivity and accelerate experimentation.

What you'll do

• 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.

What you'll bring (must-haves)

• 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.

Bonus points

• 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.

Pay Transparency:

The anticipated base salary range for this position is $240,000 to $290,000. 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.