Launch Potato
Senior ML Engineer, Recommendation Systems
Launch Potato, Cincinnati, Ohio, United States, 45208
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Senior ML Engineer, Recommendation Systems
role at
Launch Potato .
Company Overview Launch Potato is a profitable digital media company reaching over 30 million 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 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.
Compensation $165,000 – $215,000 per year, paid semi‑monthly. Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. The compensation package also includes profit‑sharing bonus and competitive benefits. Future increases are based on company and personal performance.
MUST HAVE
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: You know ML architecture, deployment, and trade‑offs inside out
Experimentation Infrastructure: You set up systems for rapid testing and retraining (MLflow, W&B)
Impact‑Driven: You design models that move revenue, retention, or engagement
Collaborative: You thrive working with engineers, PMs, and analysts to scope features
Analytical Thinking: You break down data trends and design rigorous test methodologies
Ownership Mentality: You own your models post‑deployment and continuously improve them
Execution‑Oriented: You deliver production‑grade systems quickly without sacrificing rigor
Curious & Innovative: You stay on top of ML advances and apply them to personalization
Equal Employment Opportunity We are proud to be 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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Senior ML Engineer, Recommendation Systems
role at
Launch Potato .
Company Overview Launch Potato is a profitable digital media company reaching over 30 million 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 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.
Compensation $165,000 – $215,000 per year, paid semi‑monthly. Base salary is set according to market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. The compensation package also includes profit‑sharing bonus and competitive benefits. Future increases are based on company and personal performance.
MUST HAVE
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: You know ML architecture, deployment, and trade‑offs inside out
Experimentation Infrastructure: You set up systems for rapid testing and retraining (MLflow, W&B)
Impact‑Driven: You design models that move revenue, retention, or engagement
Collaborative: You thrive working with engineers, PMs, and analysts to scope features
Analytical Thinking: You break down data trends and design rigorous test methodologies
Ownership Mentality: You own your models post‑deployment and continuously improve them
Execution‑Oriented: You deliver production‑grade systems quickly without sacrificing rigor
Curious & Innovative: You stay on top of ML advances and apply them to personalization
Equal Employment Opportunity We are proud to be 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.
#J-18808-Ljbffr