Launch Potato
Senior Machine Learning Engineer, Recommendation Systems
Launch Potato, Dallas, Texas, United States, 75215
Senior Machine Learning Engineer, Recommendation Systems
Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. We connect consumers with the world’s leading brands through data‑driven content and technology.
We are hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You will design, deploy, and scale ML systems that power real‑time recommendations across millions of user journeys, serving 100M+ predictions daily.
COMPENSATION $165,000 – $215,000 per year, paid semi‑monthly. Additional profit‑sharing bonus and competitive benefits are included.
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) and 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 recommendation systems that personalize experience for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, experimentation, and production reliability.
OUTCOMES
Build and deploy ML models serving 100M+ predictions per day
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 – set up systems for rapid testing and retraining (MLflow, W&B)
Impact‑Driven – design models that move revenue, retention, or engagement
Collaborative – work effectively with engineers, PMs, and analysts to scope features
Analytical Thinking – design rigorous test methodologies and interpret data trends
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
Additional Compensation Information Base salary is determined by market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. Future increases are based on company and personal performance, not annual cost‑of‑living adjustments.
EEO Statement Launch Potato is a performance‑driven company committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company and 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 other legally protected characteristics.
Apply now to accelerate your career!
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We are hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You will design, deploy, and scale ML systems that power real‑time recommendations across millions of user journeys, serving 100M+ predictions daily.
COMPENSATION $165,000 – $215,000 per year, paid semi‑monthly. Additional profit‑sharing bonus and competitive benefits are included.
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) and 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 recommendation systems that personalize experience for millions of users daily. You’ll own the modeling, feature engineering, data pipelines, experimentation, and production reliability.
OUTCOMES
Build and deploy ML models serving 100M+ predictions per day
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 – set up systems for rapid testing and retraining (MLflow, W&B)
Impact‑Driven – design models that move revenue, retention, or engagement
Collaborative – work effectively with engineers, PMs, and analysts to scope features
Analytical Thinking – design rigorous test methodologies and interpret data trends
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
Additional Compensation Information Base salary is determined by market rates for the nearest major metro and varies based on Launch Potato’s Levels Framework. Future increases are based on company and personal performance, not annual cost‑of‑living adjustments.
EEO Statement Launch Potato is a performance‑driven company committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company and 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 other legally protected characteristics.
Apply now to accelerate your career!
#J-18808-Ljbffr