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Launch Potato Logo

Senior ML Engineer, Recommendation Systems

Launch Potato

$130,000 - $220,000
Sep 5, 2025
Cleveland, OH, US
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Launch Potato is looking to build a personalization engine behind their portfolio of brands, including FinanceBuzz, All About Cookies, and OnlyInYourState, to connect consumers with the world’s 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
  • Partner with product, engineering, and analytics to launch high-impact personalization features

Other

  • Bachelor's degree in a relevant field (not explicitly mentioned but implied)
  • Ability to work in a remote-first team spanning over 15 countries
  • Ownership mentality and ability to own models post-deployment and continuously improve them
  • Collaborative mindset and ability to thrive working with engineers, PMs, and analysts
  • Execution-oriented and ability to deliver production-grade systems quickly without sacrificing rigor