Staff Machine Learning Engineer, ML Platform

What you'll do

• Lead complex infrastructure initiatives from design through production, including model-serving systems, customer-specific model pipelines, and CI/deployment tooling.
• Set technical direction and production quality standards for ML systems; lead incident response and improve reliability and cost efficiency.
• Coordinate platform initiatives with partner teams, review designs and code, mentor engineers and data scientists, and connect technical decisions to business outcomes.

What they ask for

• 8+ years building and operating distributed systems in production, including deployment and operations.
• Production experience with ML workloads, such as training pipelines, model serving, feature systems, or ML platform tooling.
• Technical leadership experience directing a team, leading cross-team initiatives, and developing senior engineers.
• Deep working knowledge of Kubernetes and cloud infrastructure, including identity and access management, networking, and cost.

Nice to have

• Queueing or orchestration systems such as Celery, RabbitMQ, Kafka, or Ray; ML platform tools such as MLflow, model registries, feature stores, or ML observability.
• Experience with Python, Ruby on Rails, MongoDB, and Redis; SOX or HIPAA compliance; or customer engagement, personalization, or marketing technology.

Pay

$184,000 and $314,000/year USD base salary; $204,000 and $348,000/year USD total pay (OTE, including bonus or commission)

Tools

Kubernetes, Celery, RabbitMQ, Kafka, Ray, MLflow, Python, Ruby on Rails

AI focus

Build and operate ML and AI-enhanced marketing solutions, including model training pipelines, model serving, and platform tooling. The posting also states that AI-assisted tools are used in recruiting.