Senior ML Engineer Recommendation Systems
@ Launch Potato

Hybrid
$170,000
Hybrid
Full Time
Posted 23 days ago

Your Application Journey

Personalized Resume
Apply
Email Hiring Manager
Interview

Email Hiring Manager

XXXXXXXX XXXXXXXXX XXXXXXXXX***** @launchpotato.com
Recommended after applying

Job Details

About Launch Potato

Launch Potato is a profitable digital media company connecting consumers with top brands via data-driven content and technology. With a remote-first team spanning over 15 countries and headquarters in South Florida, the company reaches over 30M+ monthly visitors across various brands.

Why Join Us?

At Launch Potato, you will accelerate your career by owning outcomes, moving fast, and driving impact with a global team. You will work on converting audience attention into action using data, machine learning, and continuous optimization.

Your Role as a Senior ML Engineer Recommendation Systems

You will design, deploy, and scale machine learning systems to power real-time personalized recommendations. This role involves owning the modeling, feature engineering, data pipelines, and experimentation behind our recommendation engine that serves over 100M+ predictions daily. Your work directly impacts engagement, retention, and revenue.

Must Have

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

Your Outcomes

  • Build and deploy ML models for 100M+ daily predictions
  • Enhance data pipelines with efficiency improvements (Spark, Beam, Dask)
  • Design ranking algorithms balancing relevance, diversity, and revenue
  • Deliver personalization with latency under 50ms
  • Run rigorous A/B tests to measure business impact
  • Optimize models for latency, throughput, and cost
  • Collaborate with product, engineering, and analytics teams
  • Implement monitoring systems and maintain model reliability

Competencies

  • Technical Mastery in ML architecture and deployment
  • Experience in rapid experimentation frameworks (MLflow, W&B)
  • Impact-driven modeling for revenue, retention, and engagement
  • Collaborative work with cross-functional teams
  • Strong analytical thinking and rigorous testing methodology
  • Ownership mentality for continuous model improvement
  • Fast execution with production-grade systems
  • Curious and innovative in applying ML advancements

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.

Diversity and Inclusion

Launch Potato is an Equal Employment Opportunity company committed to diversity, equity, and inclusion.

Want to accelerate your career? Apply now!

Key Skills/Competency

  • Machine Learning
  • Recommendation Systems
  • Ranking Algorithms
  • Python
  • Data Engineering
  • Distributed Computing
  • Deep Learning
  • A/B Testing
  • Experimentation
  • Cross-functional Collaboration

How to Get Hired at Launch Potato

🎯 Tips for Getting Hired

  • Customize your resume: Tailor it to highlight ML and recommendation experience.
  • Showcase projects: Include large-scale ML deployments and system scalability.
  • Research Launch Potato: Understand their brands and data-driven culture.
  • Practice technical interviews: Prepare for ML algorithm and system design questions.

📝 Interview Preparation Advice

Technical Preparation

Review large-scale ML system architecture.
Practice deploying models with TensorFlow/PyTorch.
Brush up on ranking algorithm design.
Familiarize with distributed frameworks like Spark.

Behavioral Questions

Describe a challenging ML project.
Explain teamwork in cross-functional settings.
Detail your ownership of system outcomes.
Discuss handling fast-paced project changes.

Frequently Asked Questions