Senior ML Engineer Recommendation Systems
@ Launch Potato

Hybrid
$175,000
Hybrid
Full Time
Posted 23 days ago

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XXXXXXXXX XXXXXXXXXXX XXXXXXX****** @launchpotato.com
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Job Details

About Launch Potato

Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors through brands like FinanceBuzz, All About Cookies, and OnlyInYourState. As The Discovery and Conversion Company, our mission is to connect consumers with top brands through data-driven content and technology. Headquartered in South Florida with a remote-first global team spanning 15 countries, we have built a high-growth, high-performance culture where speed, ownership, and measurable impact drive success.

Why Join Us?

At Launch Potato, you will accelerate your career by owning outcomes, moving fast, and driving impact alongside a global team of high-performers. We leverage data, machine learning, and continuous optimization to convert audience attention into action.

Your Role as Senior ML Engineer Recommendation Systems

You will build the personalization engine behind our portfolio of brands by designing, deploying, and scaling ML systems that power real-time recommendations across millions of user journeys. Your work will directly impact engagement, retention, and revenue by serving over 100M+ predictions daily.

  • Develop and deploy ML models for 100M+ predictions per day.
  • Enhance data pipelines using Spark, Beam, or Dask.
  • Design ranking algorithms balancing relevance, diversity, and revenue.
  • Run rigorous A/B tests to measure business impact.
  • Collaborate with product, engineering, and analytics teams.

Key Skills/Competency

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

How to Get Hired at Launch Potato

🎯 Tips for Getting Hired

  • Tailor your resume: Highlight large-scale ML systems experience.
  • Research Launch Potato: Understand their brands and global culture.
  • Emphasize technical skills: Showcase Python, ML frameworks, and SQL expertise.
  • Prepare for interviews: Review case studies on recommendation systems.

📝 Interview Preparation Advice

Technical Preparation

Review ML deployment frameworks and TensorFlow.
Practice optimizing large-scale data pipelines.
Study ranking algorithm implementations and tests.
Revise distributed computing basics with Spark.

Behavioral Questions

Explain ownership in past projects.
Describe collaboration with cross-functional teams.
Discuss handling tight deadlines effectively.
Share examples of adapting to change.

Frequently Asked Questions