Machine Learning Engineer

Braze is a customer engagement platform that helps brands deliver personalized customer experiences. Its Predictive and Generative AI team builds ML- and AI-enhanced marketing solutions, operating production systems at global scale. Braze is headquartered in New York and has offices in Austin, Berlin, Bucharest, Chicago, Dubai, Jakarta, London, Paris, San Francisco, São Paulo, Singapore, Seoul, Sydney, and Tokyo.

• Identify and drive initiatives that transform how the team runs ML in production, including replatforming queueing and orchestration, overhauling deployment and cloud identity, and retiring infrastructure.
• Design and deliver complex infrastructure initiatives from concept through production, including multi-region model-serving fleets, pipelines that maintain hundreds of customer-specific models, and CI and deployment tooling.
• Own the platform's technical vision and production quality bar; set direction for training, deploying, serving, and observing models; lead ML systems incident response; and improve reliability and cost efficiency at scale.
• Drive cross-team initiatives involving shared infrastructure, deployment tooling, and data systems, and maintain technical relationships with partner teams.
• Improve engineering quality through design and code reviews and production readiness practices; mentor senior engineers and data scientists.
• Connect technical decisions to customer and business outcomes, and represent the team's technical perspective to product and engineering leadership.

• 8+ years building and operating distributed systems in production, with depth in deployment and operations; experience designing for scale and reliability, owning CI/CD and infrastructure as code, and operating systems under production load.
• Hands-on experience with production ML workloads, such as training pipelines, model serving, feature systems, or ML platform tooling. Deep modeling experience is a plus, not a requirement.
• Technical leadership experience setting team direction, leading multi-quarter cross-team initiatives, and developing senior engineers while maintaining a high personal output.
• Deep working knowledge of Kubernetes and cloud infrastructure, including identity and access management, networking, and cost considerations.
• Effective written and verbal communication skills, with the ability to build consensus and advance decisions.
• Bonus experience with queueing and orchestration systems such as Celery, RabbitMQ, Kafka, or Ray; ML platform tooling such as MLflow or another model registry, feature stores, or ML observability; the company's stack (Python, Ruby on Rails, MongoDB, Redis, Kubernetes); SOX or HIPAA compliance; and customer engagement, personalization, or marketing technology.

• Hybrid ways of working; the role is tagged #LI-Hybrid.
• For candidates based in the United States, base pay at the start of employment is expected to be $184,000–$314,000 per year. Expected OTE is $204,000–$348,000 per year, including bonus or commission. Offers may vary based on market location, job-related knowledge, skills, and experience. The role also qualifies for restricted stock units.
• Benefits vary by location and include competitive compensation that may include equity; retirement and Employee Stock Purchase Plans; flexible paid time off; medical, dental, vision, life, and disability plans; fertility benefits and equal paid parental leave; professional development, formal career pathing, learning platforms, and a yearly learning stipend; an in-office employee experience; community volunteering and donation matching; and Employee Resource Groups.