Launch Potato is looking to build a personalization engine behind their portfolio of brands, aiming to connect consumers with the world’s leading brands through data-driven content and technology, and drive business growth by building and optimizing recommendation systems that personalize experience for millions of users daily.
Requirements
- 5+ years building and scaling production ML systems with measurable business impact
- Experience deploying ML systems serving 100M+ predictions daily
- 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
Responsibilities
- 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 <50ms across key product surfaces
- Run statistically rigorous A/B tests to measure true business impact
- Optimize for latency, throughput, and cost efficiency in production
- Partner with product, engineering, and analytics to launch high-impact personalization features
Other
- Bachelor's degree or higher in a quantitative field (e.g., Computer Science, Mathematics, Statistics)
- Ability to work in a remote-first team spanning over 15 countries
- Strong analytical thinking and problem-solving skills
- Excellent communication and collaboration skills
- Ability to work in a high-growth, high-performance culture
