Machine Learning Engineer

About This Role

Machine Learning Engineers build, deploy, and continuously improve intelligent systems that help the business make better decisions at scale. This role involves owning the complete lifecycle, from understanding business problems and developing models to deploying them into production, monitoring performance, and iterating based on real-world results. The team combines machine learning, data engineering, and modern AI techniques to transform complex customer and revenue data into products and insights.

Responsibilities

• Design, build, and deploy machine learning models that predict key business outcomes such as churn, expansion, conversion, and customer engagement
• Develop and maintain scalable ML pipelines that continuously ingest data, retrain models, validate performance, and serve predictions in production
• Work with large-scale structured and unstructured data, applying modern ML and LLM techniques where they deliver measurable business value
• Monitor production models, improve performance over time, and ensure reliability through testing, validation, and continuous iteration
• Collaborate with Data Engineers, Product Managers, and business stakeholders to deliver ML solutions that solve real customer and business problems
• Own machine learning solutions end-to-end—from problem definition and experimentation to production deployment, monitoring, and ongoing optimization

Requirements

• 3–5 years of professional experience in Machine Learning Engineering, Applied Machine Learning, Data Science, or a closely related field
• Bachelor's degree in Computer Science, Mathematics, Statistics, Engineering, or another quantitative discipline
• You enjoy building machine learning systems that solve real business problems—not just achieving better benchmark metrics
• You've worked with imperfect, real-world datasets and understand challenges like noisy labels, feature leakage, class imbalance, and concept drift
• You write clean, production-quality Python with testing, modular design, and maintainable code
• You think beyond model performance and care about the impact your work has on users and business outcomes
• You're comfortable taking ownership of projects from initial idea through production deployment and ongoing maintenance
• You use data to guide decisions and proactively identify opportunities for new ML applications
• Strong understanding of machine learning and statistical modeling techniques, including regression, classification, survival analysis, causal inference, uplift modeling, or related methods
• Experience developing models on large-scale, real-world datasets, including feature engineering, model validation, and handling imperfect data
• Experience working with unstructured data using embeddings, fine-tuning, vector representations, or other modern NLP techniques