Launch Potato
Lead Machine Learning Engineer, Recommendation Systems
Launch Potato, New Haven, Connecticut, us, 06540
Overview
Lead Machine Learning Engineer, Recommendation Systems at Launch Potato. You will design, deploy, and scale ML systems that power real-time personalized recommendations across millions of user journeys and across our portfolio of brands. This role directly impacts engagement, retention, and revenue at scale. Responsibilities
Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. Own modeling, feature engineering, data pipelines, and experimentation to improve personalization. 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 under 50 ms across key product surfaces. 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 teams to launch high-impact personalization features. Implement monitoring systems and maintain clear ownership for model reliability. Qualifications
7+ 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. Compensation
Base salary: $130,000–$250,000 per year, paid semi-monthly. Total compensation includes base salary, profit-sharing bonus, and competitive benefits. Compensation is set according to market rates for the nearest major metro and may vary based on Launch Potato’s Levels Framework. About Launch Potato
Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors with brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. We connect consumers with leading brands through data-driven content and technology. Headquartered in South Florida with a remote-first team spanning over 15 countries, we foster a high-growth, high-performance culture where speed, ownership, and measurable impact drive success. Equal Opportunity
We are 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 legally protected characteristics.
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Lead Machine Learning Engineer, Recommendation Systems at Launch Potato. You will design, deploy, and scale ML systems that power real-time personalized recommendations across millions of user journeys and across our portfolio of brands. This role directly impacts engagement, retention, and revenue at scale. Responsibilities
Drive business growth by building and optimizing the recommendation systems that personalize experiences for millions of users daily. Own modeling, feature engineering, data pipelines, and experimentation to improve personalization. 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 under 50 ms across key product surfaces. 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 teams to launch high-impact personalization features. Implement monitoring systems and maintain clear ownership for model reliability. Qualifications
7+ 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. Compensation
Base salary: $130,000–$250,000 per year, paid semi-monthly. Total compensation includes base salary, profit-sharing bonus, and competitive benefits. Compensation is set according to market rates for the nearest major metro and may vary based on Launch Potato’s Levels Framework. About Launch Potato
Launch Potato is a profitable digital media company reaching over 30M+ monthly visitors with brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. We connect consumers with leading brands through data-driven content and technology. Headquartered in South Florida with a remote-first team spanning over 15 countries, we foster a high-growth, high-performance culture where speed, ownership, and measurable impact drive success. Equal Opportunity
We are 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 legally protected characteristics.
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