Senior ML Engineer Recommendation Systems
@ Launch Potato

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

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

About Launch Potato

Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors with brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. As The Discovery and Conversion Company, they connect consumers with world-class brands using data-driven content and technology.

Why Join Us?

At Launch Potato, you accelerate your career by owning outcomes, moving fast, and driving impact alongside a high-performing global team. Here, your work in machine learning directly impacts engagement, retention, and revenue, working with systems that deliver over 100M predictions daily.

Your Role as Senior ML Engineer Recommendation Systems

You will build, deploy, and optimize ML models powering personalized recommendations. Your responsibilities include:

  • Designing and deploying ML systems serving 100M+ predictions daily.
  • Enhancing and scaling data pipelines using Spark, Beam, or Dask.
  • Developing ranking algorithms using collaborative filtering, learning-to-rank, and deep learning.
  • Running rigorous A/B tests to measure business impact.
  • Collaborating with product, engineering, and analytics teams.

Key Skills/Competency

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

How to Get Hired at Launch Potato

🎯 Tips for Getting Hired

  • Customize your resume: Emphasize experience in ML and personalization.
  • Showcase deployment skills: Highlight large-scale production systems.
  • Research Launch Potato: Understand their digital media impact.
  • Prepare for technical tests: Focus on ranking algorithms and data pipelines.

📝 Interview Preparation Advice

Technical Preparation

Review ranking algorithm fundamentals and case studies.
Practice deploying ML pipelines with Spark and Beam.
Brush up on Python coding and TensorFlow/PyTorch usage.
Analyze real-time data processing and latency optimization.

Behavioral Questions

Describe a project you owned end-to-end.
Explain collaboration with cross-functional teams.
Discuss handling pressure in fast-paced environments.
Share approach to continuous model improvement.

Frequently Asked Questions