Lead Machine Learning Engineer, Recommendation Systems

Launch Potato; Rio de Janeiro; Híbrido; BRL 160.000 - 200.000; Tempo integral

Resumo da oferta

A dynamic tech company is seeking a Lead Machine Learning Engineer to drive business growth by optimizing recommendation systems for millions of users daily. You will build and scale ML models for personalized user experiences, impacting engagement and revenue. The ideal candidate has extensive experience in ML system production and a strong background in ranking algorithms. Join us to accelerate your career in a collaborative remote-first environment.

Qualificações

• 7+ years building and scaling production ML systems with measurable business impact.
• Strong background in ranking algorithms like collaborative filtering and deep learning.
• Experience with A/B testing platforms and logging best practices.

Responsabilidades

• Drive business growth by optimizing recommendation systems for millions of users.
• Build and deploy ML models serving 100M+ predictions per day.
• Partner with product, engineering, and analytics to launch personalization features.

Conhecimentos

Building and scaling production ML systems

Ranking algorithms

Proficiency with Python

SQL and data warehouses

Familiarity with distributed computing

Ferramentas

TensorFlow

PyTorch

Snowflake

BigQuery

Spark

Descrição da oferta de emprego

Lead Machine Learning Engineer, Recommendation Systems

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

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 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: ML architecture, deployment, and tradeoffs
• Experimentation Infrastructure: Systems for rapid testing and retraining (MLflow, W&B)
• Impact-Driven: Models that move revenue, retention, or engagement
• Collaborative: Works with engineers, PMs, and analysts to scope features
• Analytical Thinking: Breaks down data trends and designs rigorous test methodologies
• Ownership Mentality: Owns models post-deployment and continuously improves them
• Execution-Oriented: Delivers production-grade systems quickly without sacrificing rigor
• Curious & Innovative: Stays on top of ML advances and applies them to personalization

Want to accelerate your career? Apply now!

Since day one, we are committed to an 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, sexual orientation, gender identity, age, veteran status, disability, or other applicable legally protected characteristics.