Senior Machine Learning Engineer

encora10; United States; Remote; USD 150,000 - 200,000; Full time

Job summary

encora10 is seeking a senior machine learning lead to design and operate production ML systems across data ingestion, feature engineering, deployment, monitoring, and ongoing improvement. You will work with product, engineering, and data teams to deliver scalable solutions in cloud environments.

The role emphasizes distributed data processing, experimentation, and technical leadership, with a focus on production-grade ML pipelines and cross-functional collaboration.

Qualifications

• At least 5 years of industry experience building, deploying, and scaling machine learning systems.
• Bachelor’s or Master’s degree in computer science, machine learning, data science, or a related area, or equivalent practical experience.
• Strong Python, SQL, and PySpark skills for data processing.
• Hands-on experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost.
• Experience with production ML pipelines, cloud deployment, and the full model lifecycle from ingestion and training to evaluation, deployment, and monitoring.
• Practical experience with Docker, Kubernetes, and containerized ML workloads, plus the communication skills to influence cross-functional teams.

Responsibilities

• Design scalable ML systems and end-to-end solutions for cloud environments, including training, batch inference, and real-time serving.
• Build and oversee data pipelines for very large datasets, maintaining their reliability and performance.
• Manage containerized workloads using Docker and Kubernetes.
• Lead experimentation, model validation, lifecycle management, and performance measurement using tools such as MLflow or Databricks.
• Improve models through monitoring, automated retraining, bias mitigation, and performance tuning.
• Evaluate emerging ML technologies, set engineering and documentation standards, and mentor engineers.

Skills

Python

SQL

PySpark

Scikit-learn

TensorFlow

Education

Bachelor’s or Master’s in CS/ML/DS

Tools

Docker

Kubernetes

MLflow

Databricks

Kubeflow

Job description

Role overview

Lead the development and operation of production machine learning systems, from data ingestion and feature engineering through deployment, monitoring, and ongoing improvement. The work combines distributed data processing, cloud infrastructure, experimentation, and technical leadership, with collaboration across product, engineering, and data teams.

Responsibilities

• Design scalable ML systems and end-to-end solutions for cloud environments, including training, batch inference, and real-time serving.
• Build and oversee data pipelines for very large datasets, maintaining their reliability and performance.
• Manage containerized workloads using Docker and Kubernetes.
• Lead experimentation, model validation, lifecycle management, and performance measurement using tools such as MLflow or Databricks.
• Improve models through monitoring, automated retraining, bias mitigation, and performance tuning.
• Evaluate emerging ML technologies, set engineering and documentation standards, and mentor engineers.

Requirements

• At least 5 years of industry experience building, deploying, and scaling machine learning systems.
• Bachelor’s or master’s degree in computer science, machine learning, data science, or a related area, or equivalent practical experience.
• Strong Python, SQL, and PySpark skills for data processing.
• Hands-on experience with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost.
• Experience with production ML pipelines, cloud deployment, and the full model lifecycle from ingestion and training to evaluation, deployment, and monitoring.
• Practical experience with Docker, Kubernetes, and containerized ML workloads, plus the communication skills to influence cross-functional teams.

Nice to have

• Healthcare data experience; a relevant advanced degree; MLOps practices such as CI/CD, model versioning, and automated retraining; deep learning for time series, sequential data, or hierarchical modeling; model evaluation and experimentation frameworks; or Kubernetes-native ML tools such as Kubeflow, KServe, or Airflow.

Benefits and work setup

The position is listed as remote in Colombia.