Launch Potato
Senior ML Engineer, Recommendation Systems
Launch Potato, Kansas City, Missouri, United States, 64101
Senior ML Engineer, Recommendation Systems
Launch Potato
is a profitable digital media company that reaches over 30M+ monthly visitors through a portfolio of brands. As the Discovery and Conversion Company, our mission is to connect consumers with leading brands through data‑driven content and technology. Headquartered in South Florida with a remote‑first team spanning 15+ countries, we’ve built a high‑growth, high‑performance culture where speed, ownership, and measurable impact drive success.
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, serving 100M+ predictions daily. This role directly impacts engagement, retention, and revenue at scale.
Compensation Base salary: $165,000 – $215,000 per year, paid semi‑monthly. Your package includes a base salary, profit‑sharing bonus, and competitive benefits.
Requirements
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 Mission Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. Own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.
Key Outcomes
Build and deploy 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: Deep knowledge of ML architecture, deployment, and trade‑offs
Experimentation Infrastructure: Rapid testing and retraining systems (MLflow, W&B)
Impact‑Driven: Models that move revenue, retention, or engagement
Collaborative: Thrive working with engineers, PMs, and analysts to scope features
Analytical Thinking: Design rigorous test methodologies
Ownership Mentality: Own models post‑deployment and continuously improve them
Execution‑Oriented: Deliver production‑grade systems quickly without sacrificing rigor
Curious & Innovative: Stay on top of ML advances and apply them to personalization
EEO Statement Launch Potato is an Equal Employment Opportunity company. We value diversity, equity, and inclusion. We 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 applicable legally protected characteristics.
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is a profitable digital media company that reaches over 30M+ monthly visitors through a portfolio of brands. As the Discovery and Conversion Company, our mission is to connect consumers with leading brands through data‑driven content and technology. Headquartered in South Florida with a remote‑first team spanning 15+ countries, we’ve built a high‑growth, high‑performance culture where speed, ownership, and measurable impact drive success.
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, serving 100M+ predictions daily. This role directly impacts engagement, retention, and revenue at scale.
Compensation Base salary: $165,000 – $215,000 per year, paid semi‑monthly. Your package includes a base salary, profit‑sharing bonus, and competitive benefits.
Requirements
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 Mission Drive business growth by building and optimizing the recommendation systems that personalize experience for millions of users daily. Own the modeling, feature engineering, data pipelines, and experimentation that make personalization smarter, faster, and more impactful.
Key Outcomes
Build and deploy 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: Deep knowledge of ML architecture, deployment, and trade‑offs
Experimentation Infrastructure: Rapid testing and retraining systems (MLflow, W&B)
Impact‑Driven: Models that move revenue, retention, or engagement
Collaborative: Thrive working with engineers, PMs, and analysts to scope features
Analytical Thinking: Design rigorous test methodologies
Ownership Mentality: Own models post‑deployment and continuously improve them
Execution‑Oriented: Deliver production‑grade systems quickly without sacrificing rigor
Curious & Innovative: Stay on top of ML advances and apply them to personalization
EEO Statement Launch Potato is an Equal Employment Opportunity company. We value diversity, equity, and inclusion. We 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 applicable legally protected characteristics.
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