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Salary: USD 184,000 - 299,812 / annual
Braze is seeking a Staff Applied Scientist to join the Predictive and Generative AI (PGAI) team. The team's mission is to deliver engaging, personalized customer experiences through ML and AI-enhanced marketing solutions, owning the full stack from model development and training pipelines to high-throughput serving APIs.
In this hands-on staff role, you will identify and drive transformative initiatives that expand what the team can deliver—whether replatforming model training and serving infrastructure, redefining how data science ships to production, or retiring legacy systems. You'll build and ship at high velocity, carrying the most complex initiatives from design through production, including distributed model training, model lifecycle management, and multi-region customer-specific model pipelines.
You will own the team's technical vision and quality bar, setting direction across the product portfolio and ML platform while defining best practices and anticipating production issues. You'll drive cross-team initiatives spanning messaging, analytics, and data platform surfaces, maintaining technical relationships with partner teams. A key responsibility is raising engineering quality through design review, code review, and production readiness for ML systems, while mentoring other senior engineers and data scientists. You'll connect technical decisions to customer and business outcomes, representing the team's technical perspective to product and engineering leadership.
You bring 8+ years building ML systems in production with hands-on depth across data science, ML engineering, and ML operations. You've designed and trained models, built production pipelines and services, and operated systems under load. You're a technical leader who has owned team direction, led multi-quarter cross-functional initiatives, and grown senior engineers while maintaining high personal output. Deep experience with predictive models (supervised/unsupervised learning, neural networks, recommenders) using PyTorch and TensorFlow is essential. Strong distributed systems fundamentals, designing for scale, reliability, and cost on billions of daily data points, are required. You're an effective communicator whose designs and recommendations build consensus and drive decisions.
Bonus qualifications include recommender systems, multi-armed bandits, or uplift modeling in production; ML platform tooling (MLflow, model registries, Ray, feature stores, ML observability); experience with Braze's stack (Python, Ruby on Rails, MongoDB, Redis, Kubernetes); and customer engagement, personalization, or marketing technology domain experience.
