Overview 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 over 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. We’re hiring a Machine Learning Engineer (Recommendation Systems) to build the personalization engine behind our portfolio of brands. You’ll design, deploy, and scale ML systems that power real-time recommendations across millions of user journeys, including 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 You’ve shipped large-scale ML systems into production that power personalization at scale. You’re 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 experiences 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 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 Potato’s 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.

Want to accelerate your career? Apply now!

We are an Equal Employment Opportunity employer and value diversity, equity, and inclusion. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, gender identity, age, veteran status, disability, or other protected characteristics.

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