(USA) Principal, Machine Learning Engineer

Position Summary...

What you'll do...

• Architect and lead the development of scalable, production-grade ML systems powering customer-facing personalization and recommendation experiences.
• Own technical direction across the end-to-end ML lifecycle, including data and feature pipelines, model training, evaluation, deployment, serving, monitoring, and continuous improvement.
• Design high-performance batch and real-time ML architectures capable of operating reliably at large scale.
• Partner with Data Scientists and other ML practitioners to translate model prototypes and experimentation into scalable, maintainable production systems.
• Establish engineering patterns and best practices for model deployment, feature management, reproducibility, automated testing, observability, retraining, and model lifecycle management.
• Develop and optimize ML solutions using techniques such as recommendation and ranking, deep learning, representation learning, NLP, and other advanced machine learning approaches.
• Design systems that balance model quality with production requirements such as latency, throughput, scalability, reliability, maintainability, and cost.
• Build reusable ML capabilities, services, and infrastructure that accelerate development and adoption across teams.
• Define technical strategies and architecture for complex and ambiguous ML problems and influence engineering decisions across multiple teams.
• Partner cross-functionally with ML, Software Engineering, Data Science, Product, and Platform teams to align ML capabilities with customer and business objectives.
• Mentor senior technical talent, raise engineering standards, and provide technical leadership across the broader ML engineering community.

What You'll Bring

• Extensive experience designing, building, deploying, and operating large-scale machine learning systems in production.
• Strong software engineering fundamentals and proficiency in Python and production software development practices.
• Deep understanding of the end-to-end ML lifecycle, including data preparation, feature engineering, model development, training, evaluation, deployment, inference, monitoring, and retraining.
• Experience building production ML systems using frameworks such as PyTorch, TensorFlow, Scikit-learn, XGBoost, or comparable technologies.
• Expertise in one or more relevant ML domains such as recommendation and ranking, deep learning, representation learning, NLP, reinforcement learning, or related advanced ML techniques.
• Experience designing scalable batch and/or real-time model training, inference, and feature-serving architectures.
• Strong understanding of distributed systems, APIs and services, cloud-native architectures, and scalable data processing.
• Experience with production ML engineering practices including automated pipelines, CI/CD, model and data versioning, testing, observability, model performance monitoring, and drift detection.
• Experience designing ML systems for performance, reliability, scalability, latency, and cost efficiency.
• Experience building and deploying production ML systems on cloud platforms such as GCP, Azure, or comparable environments, including scalable training, inference, and ML pipelines.
• Demonstrated ability to establish technical direction, influence architecture across teams, and drive complex initiatives from concept through production.
• Proven ability to mentor engineers, establish engineering best practices, and raise technical standards across an organization.
• Strong technical communication skills with the ability to communicate complex ML and engineering concepts clearly to technical and non-technical stakeholders.

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Minimum Qualifications...

Outlined below are the required minimum qualifications for this position. If none are listed, there are no minimum qualifications.

Preferred Qualifications...

Outlined below are the optional preferred qualifications for this position. If none are listed, there are no preferred qualifications.

Primary Location...