Launch Potato is looking to build the personalization engine behind its portfolio of brands to drive business growth by building and optimizing recommendation systems that personalize experience for millions of users daily.
Requirements
- 7+ 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
- Experience with A/B testing platforms and experiment logging best practices
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
- You own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.
- Partner with product, engineering, and analytics to launch high-impact personalization features
- Technical Mastery: You know ML architecture, deployment, and tradeoffs inside out
- Impact-Driven: You design models that move revenue, retention, or engagement
- Collaborative: You thrive working with engineers, PMs, and analysts to scope features
