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Senior Machine Learning Engineer Recommendation Systems

Launch Potato

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Original Job Summary

Overview

Launch Potato, a profitable digital media company with over 30M monthly visitors, is seeking a Senior Machine Learning Engineer Recommendation Systems to build and optimize personalization engines across its brands.

Why Join Launch Potato?

Accelerate your career by owning outcomes, moving fast, and driving impact in a high-performance, remote-first environment spanning 15+ countries.

Your Role

Design, deploy, and scale ML models to power real-time, personalized recommendations. You will own the modeling, feature engineering, data pipelines, and experimentation needed to serve over 100M predictions daily.

Key Responsibilities

  • Build and deploy ML models for 100M+ daily predictions.
  • Enhance data processing pipelines using Spark, Beam, or Dask.
  • Design ranking algorithms balancing relevance, diversity, and revenue.
  • Deliver real-time personalization with <50ms latency.
  • Run statistically rigorous A/B tests and monitor model reliability.
  • Collaborate with product, engineering, and analytics teams.

Must Have Qualifications

  • 5+ years of building and scaling production ML systems.
  • Experience deploying systems serving 100M+ predictions daily.
  • Strong expertise in ranking algorithms and personalization.
  • Proficiency in Python, TensorFlow/PyTorch, SQL and data warehouses.
  • Familiarity with distributed computing frameworks like Spark, Ray.

Competencies

Technical mastery, experimentation infrastructure, impact-driven design, collaborative teamwork, and strong analytical thinking.

Key skills/competency

  • Machine Learning
  • Recommendation Systems
  • Data Pipelines
  • Ranking Algorithms
  • Python
  • TensorFlow
  • PyTorch
  • SQL
  • Distributed Computing
  • A/B Testing

How to Get Hired at Launch Potato

🎯 Tips for Getting Hired

  • Research Launch Potato's culture: Explore mission, remote team, and high-performance values.
  • Tailor your resume: Emphasize production ML system experience and ranking algorithms.
  • Showcase measurable impact: Highlight successful personalization projects and KPIs.
  • Prepare for technical interviews: Practice coding, ML frameworks, and data pipelines.
  • Be ready for behavioral questions: Demonstrate teamwork and ownership examples.

📝 Interview Preparation Advice

Technical Preparation

Review ranking algorithm concepts.
Practice Python coding and ML frameworks.
Study real-time data pipeline construction.
Optimize model latency and throughput.

Behavioral Questions

Describe a time when you owned a project.
Explain collaboration with cross-functional teams.
Discuss handling unexpected ML model challenges.
Illustrate your approach to rapid experimentation.