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Salary: USD 184,000 - 332,400 / annual
Braze is seeking a Staff Applied Scientist to join the Predictive and Generative AI (PGAI) team, which delivers ML and AI-enhanced marketing solutions for customer engagement at scale. The team owns solutions end-to-end, from model development and flexible training pipelines to high-throughput APIs serving predictions into messaging systems.
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 will build and ship at high velocity, carrying the most complex initiatives from design through production, including distributed model training and serving, model lifecycle management, and pipelines managing hundreds of customer-specific models across regions.
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 will drive cross-team initiatives spanning messaging, analytics, and data platform surfaces, maintaining technical relationships with partner teams. You will raise engineering quality through design review, code review, and production readiness for ML systems, mentoring senior engineers and data scientists. You will 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 have designed and trained models, built production pipelines and services, and operated them under load. You are a technical leader who has owned team direction, led multi-quarter cross-functional initiatives, and grown senior engineers while maintaining high personal output. You have deep experience prototyping, refining, and deploying predictive models (supervised/unsupervised learning, neural networks, recommenders) using PyTorch and TensorFlow. You have strong distributed systems fundamentals, designing for scale, reliability, and cost on billions of daily data points. You are an effective communicator whose designs and recommendations build consensus and drive decisions.
Bonus experience includes recommender systems, multi-armed bandits, uplift modeling in production, ML platform tooling (MLflow, model registries, Ray, feature stores, ML observability), Braze's stack (Python, Ruby on Rails, MongoDB, Redis, Kubernetes), and customer engagement or marketing technology domain knowledge.
