Button’s mission is to empower the companies shaping the creator and affiliate economy - fueling mobile growth with innovation and new paths to monetization. Backed by Norwest and Redpoint.
About the role
As a Senior Machine Learning Engineer, you will own the end to end ML lifecycle at Button, from the data and feature pipelines that feed models, through training and evaluation workflows, to deployment, scoring, monitoring, and retraining. You will work closely with product managers, data scientists, and software engineers to translate ambiguous business problems into reliable production ML systems that integrate with our platform and power real product decisions.
What they're looking for
- 5+ years of professional experience in machine learning engineering, software engineering, data engineering, or similar roles
- Fluency with Python and SQL
- Proven experience designing, building, and operating data pipelines at scale
- Hands on experience deploying and maintaining machine learning models in production environments
- Experience working in cloud environments, especially AWS
- Familiarity with orchestration and data modeling tools such as Airflow, dbt, or similar systems
More about this role
As a Senior Machine Learning Engineer, you will own the end to end ML lifecycle at Button, from the data and feature pipelines that feed models, through training and evaluation workflows, to deployment, scoring, monitoring, and retraining. You will work closely with product managers, data scientists, and software engineers to translate ambiguous business problems into reliable production ML systems that integrate with our platform and power real product decisions.
You will help build the infrastructure and patterns that allow models to move quickly from research to production while meeting real world requirements for latency, scalability, cost efficiency, reproducibility, and safety. This role works closely with our Data Teams and plays a central role in how machine learning powers Button’s commerce and monetization products.
- Own the full ML lifecycle including feature pipelines, training workflows, model deployment, inference services, monitoring, and retraining.
- Design and build reliable data and feature pipelines, including feature store patterns that support reproducible training and consistent features across training, batch scoring, and online inference.
- Build and...
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