Launch Potato is seeking a Machine Learning Engineer to build and optimize recommendation systems that power personalization for millions of users across its portfolio of brands, aiming to drive engagement, retention, and revenue at scale.
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
- Implement monitoring systems and maintain clear ownership for model reliability
Other
- Experience with A/B testing platforms and experiment logging best practices
- Collaborative: You thrive working with engineers, PMs, and analysts to scope features
- Analytical Thinking: You break down data trends and design rigorous test methodologies
- Ownership Mentality: You own your models post-deployment and continuously improve them
- Curious & Innovative: You stay on top of ML advances and apply them to personalization
