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Salary: USD 184,000 - 299,812 / annual
Braze is seeking a Staff Machine Learning Engineer 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'll build and ship at high velocity, 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'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 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.
Required: 8+ years building production ML systems 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 prototyping, refining, and deploying predictive models (supervised/unsupervised learning, neural networks, recommenders) using PyTorch and TensorFlow. Strong distributed systems fundamentals for designing scalable, reliable, cost-effective systems handling billions of daily data points. Excellent communicator whose designs and recommendations build consensus.
Bonus: recommender systems, multi-armed bandits, uplift modeling; ML platform tooling (MLflow, model registries, Ray, feature stores, ML observability); experience with Python, Ruby on Rails, MongoDB, Redis, Kubernetes; customer engagement, personalization, or marketing technology domain knowledge.
