Senior Machine Learning Engineer

Summary

Join GoDaddy's Commerce Data & ML team as a Senior Machine Learning Engineer and build production-grade pipelines and scalable ML systems. You will design, build, and maintain robust ML pipelines for batch and real-time inference, collaborate with Data Scientists and Engineers, enhance CI/CD pipelines for ML models, and lead the end-to-end delivery of ML features. This remote position based in Colombia requires 7+ years in software with at least 5 years focused on ML engineering. You will work with AWS SageMaker, Tecton, Python, and various ML frameworks. The role involves supporting operational excellence and mentoring junior engineers. GoDaddy offers a range of benefits including paid time off, retirement savings, bonuses, equity grants, health benefits, and parental leave.

Requirements

• 7+ years in the software industry, with at least 5 years focused on ML engineering, pipelines, or applied machine learning
• Hands-on experience with AWS SageMaker Studio, Tecton, and proficiency in Python and ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow)
• Experience with experiment tracking (MLflow), orchestrating ML workflows (SageMaker Pipelines, EventBridge), and building/monitoring both batch and real-time inference pipelines
• Familiarity with data preparation and streaming tools (Glue, EMR, Flink) for feature pipelines
• Solid understanding of CI/CD for ML (model deployment, endpoint configuration, validation) and strong cross-functional collaboration skills with Data Science, Risk, and Engineering teams

Responsibilities

• Design, build, and maintain robust ML pipelines for both batch and real-time inference using tools such as SageMaker Pipelines, EventBridge, mlflow, and feature stores
• Collaborate with Data Scientists, Engineers, and stakeholders to implement, monitor, and optimize ML workflows—including model training, evaluation, drift detection, and deployment
• Enhance and manage CI/CD pipelines for ML models, oversee model promotion and retraining workflows, and ensure seamless integration across different environments
• Lead the end-to-end delivery of ML features from architecture and implementation to monitoring and iteration, while guiding and mentoring junior engineers and promoting best practices
• Support operational excellence by participating in on-call rotations, production incident response, post-mortems, and contributing to the planning and scoping of ML initiatives

Preferred Qualifications

• Experience building dual-model pipelines (e.g., NeuralNet + XGBoost) and evaluating ensemble performance
• Knowledge of business-aligned success metrics like recall, hit rate, false positives, and model drift thresholds
• Experience with fraud detection, risk modeling, or financial domains
• Familiarity with prompt engineering, LLM integration, and evaluation workflows for generative AI
• Backend or data engineering skills in Java, Scala, or Go (nice to have)

Benefits

• Paid time off
• Retirement savings (e.g., 401k, pension schemes)
• Bonus/incentive eligibility
• Equity grants
• Participation in our employee stock purchase plan
• Competitive health benefits
• Other family-friendly benefits, including parental leave