Launch Potato is seeking a Machine Learning Engineer to build and scale the personalization engine for its digital media brands, aiming to connect consumers with leading brands through data-driven content and technology.
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
- Collaborate with product, engineering, and analytics to launch high-impact personalization features
- Own models post-deployment and continuously improve them
- Set up systems for rapid testing and retraining (MLflow, W&B)
- Thrive working with engineers, PMs, and analysts to scope features
