Lead ML Engineer, Recommendation Systems

Launch Potato; Municipio de Rosario; A distancia; ARS 12.500.000 - 15.000.000; Jornada completa

Descripción de la vacante

A profitable digital media company is seeking a Machine Learning Engineer to develop a personalization engine. The ideal candidate will have extensive experience with ML systems delivering over 100M predictions daily. You will design and deploy models to enhance user engagement and retention. This role emphasizes innovation and collaboration within a remote-first team, offering the chance to directly influence revenue and user experience.

Formación

• 7+ years of experience building and scaling production ML systems.
• Experience with ML systems serving 100M+ predictions daily.
• Strong background in ranking algorithms.

Responsabilidades

• Drive business growth through recommendation systems.
• Build and optimize ML models for personalization.
• Enhance data processing pipelines.

Conocimientos

Building and scaling production ML systems

Ranking algorithms

Python and ML frameworks

SQL and data warehouses

Distributed computing

A/B testing platforms

Herramientas

TensorFlow

PyTorch

Spark

Ray

Descripción del empleo

Overview

Launch Potato is a profitable digital media company that reaches over 30M+ monthly visitors through brands such as FinanceBuzz, All About Cookies, and OnlyInYourState. 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. This role gives you the chance to work on systems serving 100M+ predictions daily, directly impacting engagement, retention, and revenue at scale.

MUST HAVE

• 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

Your role

Your mission: Drive business growth by building and optimizing the recommendation systems that personalize experience 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 <50ms 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 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

Equal Opportunity

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 applicable legally protected characteristics. We are committed to a diverse, inclusive team and culture.