Button

Senior Machine Learning Engineer

Remote Machine Learning Engineer role with clear candidate location fit.

PostedJul 18, 2026

Eligible countries1 accepted country

Seniority signalSenior

Work settingRemote

Accepted candidate locations

USA

Role overview

Readable role content extracted into sections for faster review.

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.

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 cloud environments, especially AWS. Familiarity with Amazon SageMaker, Redis, Spark, streaming systems, or distributed data processing frameworks is a plus.
Have experience with machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn.
Enjoy collaborating closely with engineers, data scientists, and product managers and can clearly communicate technical tradeoffs and design decisions.
Are comfortable working in ambiguous problem spaces and translating product questions into measurable ML solutions.

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.
Experience building ranking, recommendation, or decisioning systems is a plus.

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