Senior ML Engineer Recommendation Systems
@ Launch Potato

Hybrid
$150,000
Hybrid
Full Time
Posted 23 days ago

Your Application Journey

Personalized Resume
Apply
Email Hiring Manager
Interview

Email Hiring Manager

XXXXXXXXXX XXXXXXXXXXX XXXXXXXX******* @launchpotato.com
Recommended after applying

Job Details

About Launch Potato

Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. Headquartered in South Florida with a remote-first global team spanning 15 countries, the company connects consumers with leading brands through data-driven content and technology.

Why Join Us?

Accelerate your career by owning outcomes, moving fast, and driving impact within a high-performance culture. As a Senior ML Engineer Recommendation Systems, you will leverage data, machine learning, and continuous optimization to build the personalization engine powering multiple brands.

Your Role

Drive business growth by designing, deploying, and scaling ML models that serve 100M+ predictions daily. You will own the modeling, feature engineering, data pipelines, and experimentation that refine personalized experiences. Key outcomes include:

  • Deploy ML models serving 100M+ predictions daily.
  • Enhance data processing pipelines using Spark, Beam, and Dask.
  • Design ranking algorithms balancing relevance, diversity, and revenue.
  • Deliver real-time personalization with latency under 50ms.
  • Implement rigorous A/B testing and monitoring for model reliability.

Must Have Technical Expertise

Applicants must have over 5 years of experience building and scaling production ML systems. Requirements include expertise in ranking algorithms, proficiency in Python and ML frameworks (TensorFlow or PyTorch), SQL, modern data warehouses (Snowflake, BigQuery, Redshift), distributed computing (Spark, Ray), and familiarity with LLM/AI Agent frameworks.

Competencies

The ideal candidate demonstrates technical mastery, strong analytical thinking, experimentation infrastructure knowledge, and an ownership mentality. Collaboration with cross-functional teams (engineering, product, analytics) is key.

Total Compensation

Base salary is set according to market rates and includes profit-sharing bonuses and competitive benefits. Future increases depend on company and personal performance.

Key skills/competency

  • Machine Learning
  • Recommendation Systems
  • Ranking Algorithms
  • Python
  • Data Pipelines
  • Production ML
  • A/B Testing
  • Distributed Computing
  • Data Warehousing
  • Model Deployment

How to Get Hired at Launch Potato

🎯 Tips for Getting Hired

  • Research Launch Potato's culture: Study their mission, team, and achievements online.
  • Customize your resume: Emphasize ML deployment and ranking algorithms.
  • Highlight real impact: Showcase large-scale ML systems experience.
  • Practice technical interviews: Focus on Python, TensorFlow, and Spark challenges.

📝 Interview Preparation Advice

Technical Preparation

Review Python and TensorFlow fundamentals.
Practice ML model deployment scenarios.
Study ranking algorithm implementations.
Refine skills in distributed data processing.

Behavioral Questions

Discuss a time you owned a project.
Describe handling tight deadlines and impact.
Explain cross-team collaboration experiences.
Share problem-solving in complex projects.

Frequently Asked Questions