Hiring: Staff Applied Scientist Company: Braze Location: Austin, TX Job Posted Time: 2026-09-03 10:37:29 Employment Type: Hybrid Target Skills & Keywords : Feature Store, Kubernetes, MLflow, Model Registry, MongoDB, PyTorch, Python, Rails, Redis, TensorFlow About the job Experience: •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 yourself, built the pipelines and services that run them, and operated them under production load Required Skills: •Identify and drive the transformative initiatives that change what the team can deliver, whether that's replatforming how we train and serve models, redefining how data science ships to production, or retiring a generation of infrastructure •Build and ship at high velocity. Staff at Braze is a hands-on delivery role; you carry the most complex initiatives yourself from design through production. Current examples include distributed model training and serving, model lifecycle management, and the pipelines that keep hundreds of customer-specific models healthy across regions •Own the team's technical vision and quality bar. Set direction across the product portfolio and the ML platform, define best practices, and anticipate problems before they reach production •Drive initiatives that span teams. Our solutions ship into messaging, analytics, and data platform surfaces, and you carry the technical relationships with those teams •Raise the team's engineering quality through design review, code review, and production readiness for ML systems, and mentor other 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 Qualifications: •A technical leader who has owned direction for a team, led multi-quarter initiatives across team boundaries, and grown senior engineers, all while keeping a high personal output •Deep experience prototyping, refining, and deploying predictive models (supervised and unsupervised learning, neural networks, recommenders) with frameworks such as PyTorch and Tensorflow •Strong distributed systems fundamentals, designing for scale, reliability, and cost on the billions of daily data points our customers generate •An effective communicator, both verbal and written, whose designs and recommendations build consensus and drive forward decision making •Recommender systems, multi-armed bandits, or uplift modeling in production •ML platform tooling such as MLflow or another model registry, Ray, feature stores, or ML observability •Customer engagement, personalization, or marketing technology domain experience •For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $184,000 and $299,812/year with an expected On Target Earnings ( Compensation: •Competitive benefits and rewards package •Competitive compensation that may include equity Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!