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Lead ML Engineer, Recommendation Systems

Launch Potato

Valparaíso

A distancia

CLP 77.745.000 - 116.619.000

Jornada completa

Hace 24 días

Descripción de la vacante

A leading digital media company is seeking a Machine Learning Engineer (Recommendation Systems) to build personalization engines for their portfolio. The role involves designing, deploying, and scaling ML systems to power millions of recommendations daily. Candidates should have 7+ years of experience in creating impactful ML systems and proficiency in Python, SQL, and distributed computing frameworks. This role is part of a remote-first team focused on high performance and measurable impact.

Servicios

Diverse and inclusive team
Remote-first work environment
Career growth opportunities

Formación

  • 7+ years building and scaling production ML systems.
  • Experience deploying systems for 100M+ predictions daily.
  • Strong background in ranking algorithms.
  • Proficiency with Python and ML frameworks.
  • Skilled with SQL and modern data warehouses.

Responsabilidades

  • Drive business growth by optimizing recommendation systems.
  • Build and deploy ML models serving 100M+ daily predictions.
  • Enhance data processing pipelines with efficiency improvements.
  • Run A/B tests to measure true business impact.

Conocimientos

Production ML systems
Ranking algorithms
Python and ML frameworks
SQL and modern data warehouses
Distributed computing
A/B testing

Herramientas

TensorFlow
PyTorch
Spark
Ray
MLflow
BigQuery
Snowflake
Redshift
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.

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

Want to accelerate your career? Apply now!

Since day one, we've 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.

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.

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