Senior Machine Learning Engineer

Button is a company focused on empowering the creator and affiliate economy through innovative mobile solutions. As a Senior Machine Learning Engineer, you will manage the entire ML lifecycle, collaborating with various teams to develop and optimize machine learning models that enhance Button's commerce and monetization products.

Responsibilities:

• 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 optimize machine learning models including regression, classification, ranking, and recommender systems
• Implement and manage batch scoring pipelines and online inference services with clear performance, reliability, and latency standards
• Partner with data scientists to operationalize models and build the tooling needed to run consistent evaluation, experimentation, and model iteration
• Collaborate with software engineers to ensure smooth integration of models into production services and APIs
• Establish observability for ML systems including monitoring of data freshness, feature drift, model performance, and pipeline health
• Design systems that support rapid experimentation and safe rollout of new models
• Document architecture clearly, establish best practices for ML engineering at Button, and mentor teammates through thoughtful code reviews and design discussions
• Contribute to the design of decisioning systems that power ranking, recommendations, and commerce optimization across Button’s platform

Requirements:

• 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
• Write clear, maintainable code with strong software engineering practices including testing, documentation, debugging, and thoughtful system design
• Have experience building and operating production machine learning systems rather than only training models
• Understand the full ML lifecycle including feature generation, training pipelines, deployment strategies, and monitoring
• Have practical experience designing scalable data pipelines and feature generation workflows
• Have experience building or working with feature pipelines or feature stores that support both training and online inference
• Think deeply about reliability, scalability, latency, and cost efficiency when building ML systems
• Are comfortable working in ambiguous problem spaces and translating product questions into measurable ML solutions
• Enjoy collaborating closely with engineers, data scientists, and product managers and can clearly communicate technical tradeoffs and design decisions
• Familiarity with Amazon SageMaker, Redis, Spark, streaming systems, or distributed data processing frameworks
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn
• Familiarity with orchestration and data modeling tools such as Airflow, dbt, or similar systems
• Experience building ranking, recommendation, or decisioning systems is a plus