Launch Potato
Senior ML Engineer, Recommendation Systems
Launch Potato, Denver, Colorado, United States, 80285
Senior ML Engineer, Recommendation Systems – Launch Potato
Join our remote‑first, data‑driven team to build the personalization engine behind our portfolio of brands. In this role you’ll design, deploy, and scale ML systems that power real‑time recommendations across millions of user journeys, with the chance to serve 100M+ predictions daily.
About Launch Potato
Launch Potato is a profitable digital media company that reaches over 30 million monthly visitors across brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. Headquartered in South Florida, our mission is to connect consumers with the world’s leading brands through data‑driven content and technology. Compensation
$165,000 – $215,000 per year, paid semi‑monthly. Base salary, profit‑sharing bonus, and competitive benefits. Future increases are performance‑based. Responsibilities
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 Qualifications
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 Competencies
Technical Mastery: Deep knowledge of ML architecture, deployment, and trade‑offs Experimentation Infrastructure: Build systems for rapid testing and retraining (MLflow, W&B) Impact‑Driven: Design models that move revenue, retention, or engagement Collaborative: Thrive working with engineers, PMs, and analysts to scope features Analytical Thinking: Break down data trends and 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 Equal Employment Opportunity
We are a proud 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, veteran status, disability, or any other legally protected characteristic.
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Launch Potato is a profitable digital media company that reaches over 30 million monthly visitors across brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. Headquartered in South Florida, our mission is to connect consumers with the world’s leading brands through data‑driven content and technology. Compensation
$165,000 – $215,000 per year, paid semi‑monthly. Base salary, profit‑sharing bonus, and competitive benefits. Future increases are performance‑based. Responsibilities
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 Qualifications
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 Competencies
Technical Mastery: Deep knowledge of ML architecture, deployment, and trade‑offs Experimentation Infrastructure: Build systems for rapid testing and retraining (MLflow, W&B) Impact‑Driven: Design models that move revenue, retention, or engagement Collaborative: Thrive working with engineers, PMs, and analysts to scope features Analytical Thinking: Break down data trends and 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 Equal Employment Opportunity
We are a proud 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, veteran status, disability, or any other legally protected characteristic.
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