Senior Machine Learning Engineer Recommendation...
@ Launch Potato

Hybrid
$175,000
Hybrid
Full Time
Posted 7 hours ago

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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. Headquartered in South Florida with a remote-first team spanning 15 countries, we drive success through speed, ownership, and measurable impact.

Why Join Us?

At Launch Potato, accelerate your career by taking full ownership of outcomes, moving fast, and driving impact with a global high-performance team. Work with data, cutting-edge machine learning, and continuous optimization to convert audience attention into action.

About the Role: Senior Machine Learning Engineer Recommendation Systems

You will design, deploy, and scale machine learning systems powering real-time personalized recommendations across millions of user journeys. You will own modeling, feature engineering, data pipelines, and experimentation to deliver recommendations serving over 100M predictions daily.

  • Build and deploy ML models for real-time personalization.
  • Enhance data pipelines using Spark, Beam, or Dask.
  • Design robust ranking algorithms balancing relevance and revenue.
  • Conduct rigorous A/B tests to measure business impact.
  • Partner with product, engineering, and analytics for feature launches.

Must Have

5+ years of experience building and scaling production ML systems with measurable business impact; Proven experience deploying systems serving 100M+ predictions daily; Strong expertise in ranking algorithms, including collaborative filtering, learning-to-rank, and deep learning; Proficiency in Python and ML frameworks such as TensorFlow or PyTorch; Skilled with SQL, data warehouses (Snowflake, BigQuery, Redshift) and data lakes; Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks; Track record of improving business KPIs via personalization; Experience with A/B testing platforms and experiment logging best practices.

Outcomes & Responsibilities

  • Develop ML models delivering 100M+ predictions daily.
  • Improve data processing pipelines for efficiency and reliability.
  • Design ranking algorithms focused on relevance and revenue.
  • Deliver real-time recommendations with latency under 50ms.
  • Implement monitoring systems and maintain model reliability.

Competencies

  • Technical mastery in ML architecture and deployment.
  • Strong skills in experiment infrastructure and rapid testing.
  • Impact-driven model design improving revenue, retention, and engagement.
  • Collaborative approach with cross-functional teams.
  • Analytical thinking with clear test methodologies.
  • Ownership mentality for continuous post-deployment improvements.
  • Execution-oriented in delivering production-grade systems.
  • Curiosity and innovation in applying ML advances to personalization.

Total Compensation

The base salary ranges from $130,000 to $220,000 per year, along with profit-sharing bonuses and competitive benefits. Compensation growth is performance-driven based on company and personal performance.

Equal Employment Opportunity

Launch Potato is committed to a diverse, inclusive team and culture. We value diversity, equity, and inclusion, and do not discriminate based on any protected characteristic.

Key Skills/Competency

Machine Learning, Recommendation Systems, Personalization, Python, TensorFlow, PyTorch, SQL, Spark, Data Pipelines, A/B Testing

How to Get Hired at Launch Potato

🎯 Tips for Getting Hired

  • Customize Your Resume: Highlight ML deployment experience and outcomes.
  • Research Launch Potato: Understand their brands and global impact.
  • Showcase Relevant Projects: Detail work on recommendation systems.
  • Prepare for Technical Interviews: Review ranking algorithms and data pipelines.
  • Network Strategically: Connect with current employees on LinkedIn.

📝 Interview Preparation Advice

Technical Preparation

Review ranking algorithm implementations.
Practice ML model scaling techniques.
Study distributed computing with Spark and Ray.
Refresh Python and data pipeline best practices.

Behavioral Questions

Describe a time you owned a project.
Explain how you manage cross-team communication.
Discuss handling production issues under pressure.
Share an example of rapid iterative testing.

Frequently Asked Questions