Senior Machine Learning Engineer Recommendation...
@ Launch Potato

Hybrid
$190,000
Hybrid
Full Time
Posted 7 hours ago

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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, its mission is to connect consumers with top global brands using data-driven content and technology. Headquartered in South Florida with a remote-first team spanning 15 countries, the company boasts a high-growth, high-performance culture where speed, ownership, and measurable impact drive success.

Why Join Us?

At Launch Potato, you accelerate your career by owning outcomes, moving fast, and driving impact with a global team of high-performers. The focus is on converting audience attention into action through data, machine learning, and continuous optimization.

Your Role as Senior Machine Learning Engineer Recommendation Systems

As a Senior Machine Learning Engineer Recommendation Systems, your mission is to build the personalization engine behind Launch Potato's portfolio. You will design, deploy, and scale ML systems that serve real-time recommendations across millions of user journeys, directly impacting engagement, retention, and revenue.

Key Responsibilities

  • Build and deploy ML models serving 100M+ predictions daily.
  • Enhance data processing pipelines using tools like Spark and Beam.
  • Design ranking algorithms balancing relevance, diversity, and revenue.
  • Deliver real-time personalization with latency under 50ms.
  • Run statistically rigorous A/B tests to measure business impact.
  • Optimize systems for latency, throughput, and cost efficiency.
  • Collaborate with product, engineering, and analytics teams.
  • Implement monitoring systems ensuring model reliability.

Must Have Qualifications

  • 5+ years experience scaling production ML systems with measurable impact.
  • Experience deploying ML systems with 100M+ daily predictions.
  • Strong background in ranking algorithms, including collaborative filtering and deep learning.
  • Proficient in Python and ML frameworks like TensorFlow or PyTorch.
  • Skilled with SQL and modern data warehouses (Snowflake, BigQuery, Redshift) and data lakes.
  • Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks.
  • Experience with A/B testing platforms and experiment logging best practices.

Core Competencies

  • Technical Mastery in ML architecture, deployment, and tradeoffs.
  • Experience in rapid experimentation infrastructures (MLflow, W&B).
  • Data-driven impact with models that move revenue, retention, or engagement.
  • Collaborative approach with cross-functional teams.
  • Strong analytical thinking and rigorous test methodology design.
  • Ownership mentality with continuous model improvements.
  • Execution focus delivering production-grade systems quickly.
  • Curiosity and innovative application of the latest ML advances.

Total Compensation

Base salary is set according to market rates and varies based on Launch Potato’s Levels Framework. The package includes a base salary, profit-sharing bonus, and competitive benefits. Future increases are performance-driven.

Equal Opportunity

Launch Potato is committed to diversity, equity, and inclusion. It is an Equal Employment Opportunity company and does not discriminate based on legally protected characteristics.

Key skills/competency

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

How to Get Hired at Launch Potato

🎯 Tips for Getting Hired

  • Research Launch Potato's culture: Understand its global, remote-first high-performance environment.
  • Customize your resume: Highlight large-scale ML system deployment experience.
  • Proof your skills: Emphasize ranking algorithms and personalization expertise.
  • Prepare for interviews: Practice discussing ML model optimization and A/B testing.

📝 Interview Preparation Advice

Technical Preparation

Review ranking algorithm techniques.
Practice scaling ML systems using Spark.
Brush up on TensorFlow and PyTorch.
Study data pipeline optimization strategies.

Behavioral Questions

Describe a challenging ML project.
Explain teamwork in remote settings.
Discuss handling tight deadlines under pressure.
Share experience with rapid prototyping.

Frequently Asked Questions