griddable.io
Description
About the Role
We are seeking a highly skilled Machine Learning Engineer to design, build, and productionalize models that drive customer growth, engagement and retention. This role will focus specifically on attrition prediction and mitigation – identifying customers at risk of churn and surfacing proactive interventions that improve customer satisfaction and lifetime value. You will work closely with data scientists, software engineers, product managers, and business stakeholders to build scalable ML systems that power attrition predictions, risk and mitigation explanations and next best action recommendations. Key Responsibilities
Design predictive models for user and customer attrition using supervised, unsupervised, deep learning and generative techniques. Design scalable data pipelines for feature generation from both structured and unstructured sources of product adoption, sales activity, and customer engagement data (e.g. product telemetry, usage logs, CRM, sales activity, etc.) Build and maintain production-grade ML services, integrating models into APIs or decision systems that support real-time and batch use cases. Continuously monitor and improve model performance through drift detection, retraining automation and impact measurement. Collaborate with product and engineering teams to integrate models into production systems and agentic experiences, ensuring scalability, robustness and efficiency. Mentor junior engineers and data scientists and provide technical leadership in model architecture, experimentation, and deployment best practices. What We’re Looking For
Demonstrated ability to take models from research to production Strong software engineering proficiency in Python and data manipulation skills like SQL. Experience using third-party and in-house Machine learning tools and infrastructure to develop reusable, high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep. Exposure to architectural patterns of a large, high-scale software application (e.g. well-designed APIs, high volume data pipelines, efficient algorithms, etc.) Familiarity with ML libraries such as scikit-learn, XGBoost, Pytorch, or TensorFlow Experience with feature engineering on big data (Spark, Trino, Snowflake, etc.) Experience with ML lifecycle management tools (ML Flow, Airflow, Kubeflow or equivalents). Experience with containerization technologies (Docker) and orchestration (Kubernetes). Strong grasp of model evaluation, drift monitoring and explainability best practices. Experience with Agile development methodology, Test-Driven Development, incremental delivery, and CI/CD Experience owning and operating services throughout the software development lifecycle including design, development, release and maintenance. Experience communicating technical vision, mentoring junior engineers and managing projects. Experience developing and evaluating AI Agents that integrate with traditional ML models, (e.g. combining predictive scoring systems with generative or agentic workflows to automate customer engagement flows and recommendations. Preferred Qualifications (Bonus Points)
Familiarity with retention modeling or next best action recommendation systems. Experience developing or contributing to shared ML frameworks or internal ML Ops platforms. Experience with Feature Stores like Feast
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About the Role
We are seeking a highly skilled Machine Learning Engineer to design, build, and productionalize models that drive customer growth, engagement and retention. This role will focus specifically on attrition prediction and mitigation – identifying customers at risk of churn and surfacing proactive interventions that improve customer satisfaction and lifetime value. You will work closely with data scientists, software engineers, product managers, and business stakeholders to build scalable ML systems that power attrition predictions, risk and mitigation explanations and next best action recommendations. Key Responsibilities
Design predictive models for user and customer attrition using supervised, unsupervised, deep learning and generative techniques. Design scalable data pipelines for feature generation from both structured and unstructured sources of product adoption, sales activity, and customer engagement data (e.g. product telemetry, usage logs, CRM, sales activity, etc.) Build and maintain production-grade ML services, integrating models into APIs or decision systems that support real-time and batch use cases. Continuously monitor and improve model performance through drift detection, retraining automation and impact measurement. Collaborate with product and engineering teams to integrate models into production systems and agentic experiences, ensuring scalability, robustness and efficiency. Mentor junior engineers and data scientists and provide technical leadership in model architecture, experimentation, and deployment best practices. What We’re Looking For
Demonstrated ability to take models from research to production Strong software engineering proficiency in Python and data manipulation skills like SQL. Experience using third-party and in-house Machine learning tools and infrastructure to develop reusable, high-performing Machine Learning systems, enable fast model development, low-latency serving and ease of model quality upkeep. Exposure to architectural patterns of a large, high-scale software application (e.g. well-designed APIs, high volume data pipelines, efficient algorithms, etc.) Familiarity with ML libraries such as scikit-learn, XGBoost, Pytorch, or TensorFlow Experience with feature engineering on big data (Spark, Trino, Snowflake, etc.) Experience with ML lifecycle management tools (ML Flow, Airflow, Kubeflow or equivalents). Experience with containerization technologies (Docker) and orchestration (Kubernetes). Strong grasp of model evaluation, drift monitoring and explainability best practices. Experience with Agile development methodology, Test-Driven Development, incremental delivery, and CI/CD Experience owning and operating services throughout the software development lifecycle including design, development, release and maintenance. Experience communicating technical vision, mentoring junior engineers and managing projects. Experience developing and evaluating AI Agents that integrate with traditional ML models, (e.g. combining predictive scoring systems with generative or agentic workflows to automate customer engagement flows and recommendations. Preferred Qualifications (Bonus Points)
Familiarity with retention modeling or next best action recommendation systems. Experience developing or contributing to shared ML frameworks or internal ML Ops platforms. Experience with Feature Stores like Feast
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