Offer summary

Qualifications:

Deep expertise in machine learning and software engineering., Experience in designing and deploying production-grade ML systems., Proficiency in advanced modeling techniques such as regression and classification., Strong collaboration skills to work with cross-functional teams..

Key responsibilities:

Design and build machine learning systems for various business applications.

Own the ML lifecycle from prototyping to deployment, ensuring scalability.

Develop and maintain data and feature pipelines for real-time and batch inference.

Collaborate with stakeholders to translate business needs into effective ML solutions.

Job description

You are:

A Senior Machine Learning Engineer passionate about building and deploying data-driven products that power real business impact. You bring deep expertise in machine learning and software engineering, and you're eager to work alongside data scientists, MLOps engineers, and stakeholders across the business to solve complex problems at scale. You enjoy designing ML systems that are not only predictive but production-ready, scalable, and maintainable. You thrive in fast-paced environments, are detail-oriented, and are driven by delivering measurable results through applied ML.

You will:

Design and build production-grade machine learning systems that support personalization, user engagement, fraud detection, or customer lifetime value modeling.
Own the entire ML lifecycle from prototyping to deployment, working closely with MLOps and data engineering to ensure robust and scalable infrastructure.
Apply advanced modeling techniques (regression, classification, time series forecasting, etc.) using structured and unstructured data.
Develop and maintain robust data and feature pipelines, supporting real-time and batch inference.
Benchmark model performance and lead experiments to optimize predictive power, fairness, and business value.
Document your work clearly and concisely to ensure reproducibility and facilitate collaboration across teams.
Leverage tools like MLflow to manage experiments, model versioning, and deployment.
Partner with stakeholders across product, engineering, and analytics to translate business needs into machine learning solutions.
Actively contribute to the technical excellence of the team by reviewing code, sharing knowledge, and promoting best practices.