Senior Machine Learning Engineer Recommendation...
@ Launch Potato

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

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XXXXXXXX XXXXXXXXXXXXX XXXXXXX***** @launchpotato.com
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Job Details

About Launch Potato

Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. Headquartered in South Florida with a remote-first team spanning over 15 countries, we’ve built a high-growth, high-performance culture where speed, ownership, and measurable impact drive success.

Why Join Us?

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

Your Role as Senior Machine Learning Engineer Recommendation Systems

Own the personalization engine behind our portfolio of brands by designing, deploying, and scaling ML systems that deliver real-time recommendations. Work on systems serving 100M+ predictions daily to directly impact engagement, retention, and revenue at scale.

Must Have

  • 5+ years building and scaling production ML systems
  • Experience deploying ML systems with 100M+ predictions daily
  • Expertise in ranking algorithms including collaborative filtering and deep learning
  • Proficiency in Python and ML frameworks like TensorFlow or PyTorch
  • Strong skills with SQL, modern data warehouses, and data lakes
  • Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks
  • Proven track record in improving business KPIs via ML-powered personalization
  • Experience with A/B testing platforms and experiment logging

Your Responsibilities

  • Build and deploy ML models serving 100M+ predictions per day
  • Enhance data processing pipelines with efficiency and reliability improvements
  • 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 production for latency, throughput, and cost efficiency
  • Collaborate with product, engineering, and analytics teams
  • Implement monitoring systems to ensure model reliability

Competencies

  • Technical Mastery in ML architecture and deployment
  • Expertise in Experimentation Infrastructure (MLflow, W&B)
  • Impact-Driven with focus on revenue, retention, and engagement
  • Collaborative approach with cross-functional teams
  • Analytical Thinking in breaking down data trends
  • Ownership mentality for continuous model improvement
  • Execution oriented to deliver production-grade systems quickly
  • Innovative attitude towards applying advanced ML techniques

Total Compensation

Base salary is competitive and set according to market rates with additional profit-sharing, bonus, and benefits. Future increases depend on company and personal performance.

Diversity & Inclusion

Launch Potato is an Equal Employment Opportunity company committed to diversity, equity, and inclusion. All qualified applicants are encouraged to apply regardless of race, religion, gender, or other legally protected characteristics.

Key skills/competency

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

How to Get Hired at Launch Potato

🎯 Tips for Getting Hired

  • Customize your resume: Highlight large-scale ML projects.
  • Showcase technical mastery: Emphasize ranking algorithms skills.
  • Prepare detailed case studies: Use A/B testing examples.
  • Research Launch Potato: Understand their digital media impact.

📝 Interview Preparation Advice

Technical Preparation

Review ranking algorithm implementations in Python.
Practice ML deployment on scalable infrastructures.
Study distributed computing frameworks like Spark.
Refresh deep learning model tuning techniques.

Behavioral Questions

Describe a time you solved scaling challenges.
Explain your approach to cross-team collaboration.
Detail a situation handling project ownership.
Share an experience managing tight deadlines.

Frequently Asked Questions