Moloco
Expression of Interest: Machine Learning Engineer
Menlo Park, California, United States; New York, New York, United States; Seattle, Washington, United StatesFrom $386.4kmidAdded 1 month ago
About this role
Moloco is seeking Machine Learning Engineers to design, train, and deploy large-scale models that enhance their programmatic advertising and commerce media products. This role focuses on optimizing marketplace performance by working with AI techniques to drive measurable business outcomes.
What you'll do
- Design and iterate high-performance ML models for ad relevance and conversion rates
- Productionize scalable ML pipelines for real-time systems
- Extract insights from large datasets to refine modeling strategies
- Translate business goals into modeling challenges and metrics
- Implement models for balanced marketplace health
- Enhance system reliability and improve experimentation velocity
What they're looking for
- Production ML experience at scale
- Strong understanding of ML algorithms
- Experience with data pipelines and processing
- Analytical skills for insights extraction
- Collaboration with product and data science teams
- Experience with A/B testing and evaluations
- Knowledge of marketplace dynamics
- Debugging and system monitoring skills
Benefits
- Work in a fast-paced environment
- Opportunity to innovate and scale
- Be part of a growing team in AI advertising
- Engage with massive datasets
- Contribute to next-generation machine learning systems
- Collaborative company culture
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Moloco
Moloco builds programmatic advertising and commerce media products powered by large-scale machine learning models to optimize marketplace performance. The company is hiring Machine Learning Engineers to design, train, and deploy AI systems that drive measurable business outcomes.
- Website
- moloco.com
Likely interview questions
- Walk us through your experience building and deploying ML models in production at scale. What were the biggest challenges you faced in moving from offline evaluation to online performance?
- Describe a time you had to optimize a machine learning system for both latency and throughput. How did you balance model complexity with inference speed?