Lead Machine Learning Engineer, Recommendation Systems

Overview

Lead Machine Learning Engineer, Recommendation Systems. As The Discovery and Conversion Company, our mission is to connect consumers with the worlds leading brands through data-driven content and technology. Headquartered in South Florida with a remote-first team spanning over 15 countries, weve built a high-growth, high-performance culture where speed, ownership, and measurable impact drive success. Were hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. Youll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale. BASE SALARY:

$130,000$250,000 per year, paid semi-monthly MUST HAVE

Youve shipped large-scale ML systems into production that power personalization at scale. Youre fluent in ranking algorithms and know how to turn data into engagement and conversions. Specifically: 7+ years building and scaling production ML systems with measurable business impact 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. Youll 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 tradeoffs 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 Total Compensation

Base salary is set according to market rates for the nearest major metro and varies based on Launch Potatos Levels Framework. Your compensation package includes a base salary, profit-sharing bonus, and competitive benefits. Launch Potato is a performance-driven company which means once you are hired, future increases will be based on company and personal performance, not annual cost of living adjustments. EEO and Diversity

Since day one, we have been committed to having a diverse, inclusive team and culture. We are proud to be an Equal Employment Opportunity company. We value diversity, equity, and inclusion. #J-18808-Ljbffr