Launch Potato
Senior ML Engineer, Recommendation Systems
Launch Potato, Baltimore, Maryland, United States, 21276
Overview
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Senior ML Engineer, Recommendation Systems
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Launch Potato 1 week ago Be among the first 25 applicants Get AI-powered advice on this job and more exclusive features. WHO ARE WE?
Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. As The Discovery and Conversion Company, our mission is to connect consumers with the world’s leading brands through data-driven content and technology. 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?
At Launch Potato, you’ll accelerate your career by owning outcomes, moving fast, and driving impact with a global team of high-performers. We convert audience attention into action through data, machine learning, and continuous optimization. We’re hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You’ll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale. BASE SALARY:
$130,000–$220,000 per year, paid semi-monthly
Responsibilities
Develop, deploy, and scale ML systems that power real-time recommendations across millions of user journeys Own modeling, feature engineering, data pipelines, and experimentation for personalization at scale Build and maintain production ML systems serving 100M+ predictions daily Collaborate with product, engineering, and analytics to launch high-impact personalization features
Must Have
You’ve shipped large-scale ML systems into production that power personalization at scale. You’re fluent in ranking algorithms and know how to turn data into engagement and conversions. Specifically: 5+ years building and scaling production ML systems with measurable business impact Experience deploying ML systems serving 100M+ predictions daily Strong background in ranking algorithms (collaborative filtering, learning-to-rank, deep learning) Proficiency with Python and ML frameworks (TensorFlow or PyTorch) Skilled with SQL and modern data warehouses (Snowflake, BigQuery, Redshift) plus data lakes Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks Track record of improving business KPIs via ML-powered personalization Experience with A/B testing platforms and experiment logging best practices
Your Role
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.
Outcomes
Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency
Run statistically rigorous A/B tests to measure true business impact Optimize for latency, throughput, and cost efficiency in production Partner with product, engineering, and analytics to launch high-impact personalization features Implement monitoring systems and maintain clear ownership for model reliability
Competencies
Technical Mastery: ML architecture, deployment, and tradeoffs Experimentation Infrastructure: MLflow, W&B for rapid testing and retraining Impact-Driven: Models that move revenue, retention, or engagement Collaborative: Work with engineers, PMs, and analysts to scope features Analytical Thinking: Design rigorous test methodologies Ownership Mentality: Post-deployment ownership and continuous improvement Execution-Oriented: Production-grade systems delivered quickly Curious & Innovative: Apply ML advances to personalization
Total Compensation
Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Launch Potato is a performance-driven company, with future increases based on company and personal performance, not annual cost of living adjustments.
Equal Opportunity
We are committed to diversity, equity, and inclusion. We are an Equal Employment Opportunity company and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other legally protected characteristics.
Want to accelerate your career? Apply now!
#J-18808-Ljbffr
Join to apply for the
Senior ML Engineer, Recommendation Systems
role at
Launch Potato 1 week ago Be among the first 25 applicants Get AI-powered advice on this job and more exclusive features. WHO ARE WE?
Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. As The Discovery and Conversion Company, our mission is to connect consumers with the world’s leading brands through data-driven content and technology. 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?
At Launch Potato, you’ll accelerate your career by owning outcomes, moving fast, and driving impact with a global team of high-performers. We convert audience attention into action through data, machine learning, and continuous optimization. We’re hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You’ll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale. BASE SALARY:
$130,000–$220,000 per year, paid semi-monthly
Responsibilities
Develop, deploy, and scale ML systems that power real-time recommendations across millions of user journeys Own modeling, feature engineering, data pipelines, and experimentation for personalization at scale Build and maintain production ML systems serving 100M+ predictions daily Collaborate with product, engineering, and analytics to launch high-impact personalization features
Must Have
You’ve shipped large-scale ML systems into production that power personalization at scale. You’re fluent in ranking algorithms and know how to turn data into engagement and conversions. Specifically: 5+ years building and scaling production ML systems with measurable business impact Experience deploying ML systems serving 100M+ predictions daily Strong background in ranking algorithms (collaborative filtering, learning-to-rank, deep learning) Proficiency with Python and ML frameworks (TensorFlow or PyTorch) Skilled with SQL and modern data warehouses (Snowflake, BigQuery, Redshift) plus data lakes Familiarity with distributed computing (Spark, Ray) and LLM/AI Agent frameworks Track record of improving business KPIs via ML-powered personalization Experience with A/B testing platforms and experiment logging best practices
Your Role
Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.
Outcomes
Build and deploy ML models serving 100M+ predictions per day to personalize user experiences at scale Enhance data processing pipelines (Spark, Beam, Dask) with efficiency and reliability improvements Design ranking algorithms that balance relevance, diversity, and revenue Deliver real-time personalization with latency
Run statistically rigorous A/B tests to measure true business impact Optimize for latency, throughput, and cost efficiency in production Partner with product, engineering, and analytics to launch high-impact personalization features Implement monitoring systems and maintain clear ownership for model reliability
Competencies
Technical Mastery: ML architecture, deployment, and tradeoffs Experimentation Infrastructure: MLflow, W&B for rapid testing and retraining Impact-Driven: Models that move revenue, retention, or engagement Collaborative: Work with engineers, PMs, and analysts to scope features Analytical Thinking: Design rigorous test methodologies Ownership Mentality: Post-deployment ownership and continuous improvement Execution-Oriented: Production-grade systems delivered quickly Curious & Innovative: Apply ML advances to personalization
Total Compensation
Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Launch Potato is a performance-driven company, with future increases based on company and personal performance, not annual cost of living adjustments.
Equal Opportunity
We are committed to diversity, equity, and inclusion. We are an Equal Employment Opportunity company and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other legally protected characteristics.
Want to accelerate your career? Apply now!
#J-18808-Ljbffr