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Braze

Customer engagement platform for cross-channel messaging

Staff Applied Scientist

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeAustin, TX, USA$184k - $299.8k /yrIn-person

About the Job

Requirements

  • At least 8 years of building machine learning systems in production, with hands-on depth across data science, machine learning engineering, and machine learning operations; experience designing and training models, building their pipelines and services, and operating them under production load.
  • Experience owning technical direction for a team, leading multi-quarter initiatives across team boundaries, and growing senior engineers while maintaining high personal output.
  • Deep experience prototyping, refining, and deploying predictive models, including supervised and unsupervised learning, neural networks, and recommender systems, using frameworks such as PyTorch and TensorFlow.
  • Strong distributed systems fundamentals, including designing for scale, reliability, and cost across billions of daily data points.
  • Effective verbal and written communication, with the ability to build consensus and advance decisions through designs and recommendations.

Responsibilities

  • Identify and drive transformative initiatives that expand team capabilities, such as replatforming model training and serving, changing how data science ships to production, or retiring infrastructure generations.
  • Build and ship rapidly, personally carrying complex initiatives from design through production, including distributed model training and serving, model lifecycle management, and pipelines that maintain customer-specific models across regions.
  • Own the team's technical vision and quality bar; set direction across the product portfolio and machine learning platform, define best practices, and anticipate production problems.
  • Drive initiatives spanning teams and maintain technical relationships with messaging, analytics, and data platform teams.
  • Raise engineering quality for machine learning systems through design reviews, code reviews, and production-readiness work, and mentor 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.

Desired Qualifications

  • Production experience with recommender systems, multi-armed bandits, or uplift modeling.
  • Experience with machine learning platform tooling such as MLflow or another model registry, Ray, feature stores, or machine learning observability.
  • Experience with Python, Ruby on Rails, MongoDB, Redis, or Kubernetes.
  • Experience in customer engagement, personalization, or marketing technology.

About the Company

Braze

Customer engagement platform for cross-channel messaging

Braze is a customer engagement platform that helps brands build ongoing relationships with customers by coordinating real-time, cross-channel messages across email, push, in-app, SMS, and more. It collects data from any source, segments audiences, and orchestrates customer journeys so the right message reaches the right person at the right time. It also uses AI-powered experimentation and optimization to test and improve campaigns. Braze differentiates itself with a data-driven, multi-channel approach and a strong emphasis on journey orchestration and real-time engagement, serving a wide range of clients from small businesses to large enterprises and operating globally. Its goal is to enable brands to deliver relevant, personalized experiences at scale, across channels, to drive lasting customer relationships.

Company size
1,001-5,000
Company stage
IPO
Headquarters
New York City, New York
Founded
2011

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