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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. This team delivers engaging, personalized customer experiences through ML and AI-enhanced marketing solutions, owning the full stack from model development and 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, personally carrying the most complex initiatives from design through production, including distributed model training, model lifecycle management, and pipelines maintaining 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 the pipelines and services running them, and operated them under production load. You are a technical leader who has owned team direction, led multi-quarter cross-boundary 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 understand distributed systems fundamentals, designing for scale, reliability, and cost at the billions of daily data points customers generate. You are an effective communicator whose designs and recommendations build consensus and drive decision-making.
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); or customer engagement, personalization, and marketing technology domain experience.
