ML Engineer - 18585

Role: Machine Learning Engineer 
Priority Location: LATAM
Working Hours: PST (Pacific Standard Time)

This is a full-time remote role working with a U.S.-based team. Candidates must be comfortable with some overlap with U.S. working hours.

Type of contract: Independent Contractor
Type of job: Remote

The final offer is at the client’s discretion and will depend on the candidate’s interview result, skills, and experience.

A leading marketing technology company specializing in data-driven solutions for lead generation, campaign attribution, and conversion optimization. The organization leverages advanced machine learning, automation, and analytics to deliver measurable business outcomes across large-scale platforms.

As a Machine Learning Engineer, you will design, build, and deploy production-grade ML systems that directly impact lead generation efficiency, predictive accuracy, and customer journey optimization.

This is a hands-on role focused on building scalable ML systems, time-series forecasting models, and robust data pipelines, while ensuring reliability and performance in production environments. You will collaborate cross-functionally with Engineering, Product, and Data teams to translate business needs into impactful ML solutions.

• →Build and deploy scalable ML systems for prediction tasks such as lead scoring, attribution modeling, occupancy forecasting, demand forecasting, and ROI estimation
• →Develop and maintain batch and streaming data pipelines for ML training, feature engineering, and real-time inference
• →Design and implement time-series models using deep learning approaches (LSTM, GRU, Transformers, TCNs) and classical methods (ARIMA, Prophet, ETS)
• →Productionize ML models using modern MLOps practices, including CI/CD, monitoring, model evaluation, and retraining pipelines
• →Design and manage feature stores optimized for both offline training and real-time inference
• →Integrate data from multiple sources (CRM systems, ad platforms, analytics tools) to create robust datasets
• →Collaborate with Product and business stakeholders to define use cases, run experiments, and measure performance impact
• →Explore advanced approaches such as embeddings, probabilistic forecasting, and hybrid ML/LLM architectures
• →Ensure reliability, scalability, and observability of ML systems in production

• 5+ years of experience in Machine Learning Engineering or similar roles
• Strong proficiency in Python
• Hands-on experience with:
  • PyTorch (deep learning and time-series modeling)
  • Keras (LSTM/GRU architectures, forecasting models)
  • scikit-learn, XGBoost, Random Forest, and ensemble methods
• Strong experience in time-series forecasting using both deep learning and classical approaches
• Advanced SQL skills and strong understanding of data modeling and ETL processes
• Experience with workflow orchestration tools (Airflow, Flyte, Prefect, or similar)
• Experience deploying ML models via APIs or model-serving frameworks (FastAPI, MLflow, TensorFlow Serving)
• Strong understanding of statistics, experimentation, A/B testing, and model evaluation

• Experience with marketing analytics, attribution modeling, or ad platforms
• Background in forecasting or real estate / occupancy modeling
• Familiarity with Docker, Kubernetes, or similar containerization tools
• Experience with embeddings, vector search, or LLM integrations
• Experience with GCP tools such as BigQuery or Vertex AI
• Exposure to distributed training or GPU optimization

• Strong ownership mindset with the ability to build and manage systems end-to-end
• Ability to translate business requirements into technical solutions
• Excellent cross-functional collaboration skills
• Analytical thinking with a focus on delivering measurable business impact
• Structured and detail-oriented approach to system design and implementation

• Full-time, fully remote role with a U.S.-based team
• Opportunity to work on cutting-edge ML and AI systems in a high-impact environment
• Direct influence on product and AI roadmap
• Collaborative, fast-paced, and innovation-driven culture
• Competitive compensation and growth opportunities