Lead Machine Learning Engineer, Recommendation Systems
4 semanas atrás
As The Discovery and Conversion Company, our mission is to connect consumers with the world’s leading brands through data-driven content and technology.
Headquartered in South Florida with a remote-first team spanning over 15 countries, we’ve built a high-growth, high-performance culture where speed, ownership, and measurable impact drive success.
WHY JOIN US?At Launch Potato, you’ll accelerate your career by owning outcomes, moving fast, and driving impact with a global team of high-performers.
We convert audience attention into action through data, machine learning, and continuous optimization.
We’re hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You’ll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale.
MUST HAVEYou’ve shipped large-scale ML systems into production that power personalization at scale. You’re fluent in ranking algorithms and know how to turn data into engagement and conversions. Specifically:
- 7+ years building and scaling production ML systems with measurable business impact
- Strong background in ranking algorithms (collaborative filtering, learning-to-rank, deep learning)
- Proficiency with Python and ML frameworks (TensorFlow or PyTorch)
- Skilled with SQL and modern data warehouses (Snowflake, BigQuery, Redshift) plus data lakes
- Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks
- Track record of improving business KPIs via ML-powered personalization
- Experience with A/B testing platforms and experiment logging best practices
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.
OUTCOMES- Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale
- Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements
- Design ranking algorithms that balance relevance, diversity, and revenue
- Deliver real-time personalization with latency
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