Machine Learning Engineer for Performance-Based Mobile App Marketing

Location: Remote @ LatAm (preferred: Brazil, Mexico, Chile, Argentina, or Puerto Rico) ;

·      Candidates outside LATAM will not be considered .

Work Authorization: Not applicable

Contract Duration: Full-time ;

Rate: USD 5,8K - 6.2K /month .

TL:DR

·      Machine Learning Engineer ;

·      Remote role @ U.S. company (CET time zone) ;

·      Focus on model development, fraud detection, and recommendation systems ;

·      5+ years of experience ;

·      Python (5+ years) + PyTorch, TensorFlow, scikit-learn, XGBoost, LightGBM ;

·      Experience with Spark, Airflow, dbt, MLflow and production-grade ML pipelines ;

·      Background in AdTech or performance marketing required ;

·      Not a Data Scientist role - engineers only .

About the Role

We are seeking a Machine Learning Engineer to join the Campaign Delivery team in a performance-based mobile app marketing company. The focus of this role is to build and deploy predictive models and recommendation systems that power campaign delivery, optimization, and fraud detection at scale.

You will work closely with cross-functional teams (Data Engineering, Product, and Campaign Operations) to design, train, evaluate, and deploy production-grade ML models that directly drive business outcomes. The first project will focus on automating conversion review and validation — developing real-time models to detect fraud and improve traffic quality.

What We're Looking For

·      5+ years of experience as a Machine Learning Engineer or in applied ML.

·      Expert-level Python skills, with strong experience in PyTorch and/or TensorFlow.

·      Proven experience building and deploying ML models in production (recommendation systems, conversion prediction, or CTR models).

·      Proficiency with tabular ML frameworks (scikit-learn, XGBoost, LightGBM).

·      Familiarity with feature engineering and pipelines using Spark, Airflow, and dbt.

·      Experience with experiment tracking and model lifecycle tools (MLflow).

·      Strong understanding of statistical validation, model evaluation, and A/B testing.

·      Hands-on experience deploying models through REST APIs, containers, or AWS (SageMaker, Lambda, ECS).

·      Clear communication in English with the ability to translate complex concepts for non-technical teams.

Extras That Make You Shine:

·      AdTech or performance marketing background with deep knowledge of CPI, CPA, ROAS, eCPM, and LTV.

·      Experience designing fraud detection systems (click injection, device farms, SDK spoofing, etc.).

·      Familiarity with bandit algorithms, real-time bidding (RTB), or campaign optimization modeling.

·      Knowledge of causal inference or uplift modeling approaches.

·      Comfort using AI-assisted coding tools (Copilot, Cursor, etc.).

Key Responsibilities

·      Develop and deploy ML models for fraud detection, traffic quality, and conversion validation.

·      Create and maintain recommendation systems to match campaigns with high-value users.

·      Build predictive models for targeting, pacing, and performance forecasting.

·      Collaborate with engineering teams on feature store development and model serving infrastructure.

·      Monitor production performance and design retraining workflows.

·      Run A/B tests to validate model performance and measure real-world business impact.

·      Contribute to MLOps processes, experiment tracking, and version control.

Additional Details

·      Fully remote role aligned with U.S. working hours (CST).

·      Inclusive, innovative U.S.-based team with global reach.

·      Continuous technical and professional growth opportunities.

·      Collaborative, output-driven environment that values ownership and iteration.