At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew.
We seek to ignite that passion by setting high standards, championing teamwork, and creating work-life harmony as we collectively navigate rapid growth on a global scale while striving for greater equity and opportunity – inside and outside our organization.
To flourish here, you must be prepared to set a high bar for yourself and those around you. There is always a way to contribute: Acting with autonomy, having accountability and being open to new perspectives are essential to our continued success.
Our deep curiosity to learn and our eagerness to share diverse passions with others gives us balance and injects a one-of-a-kind vibrancy into our culture.
If you are driven to solve exhilarating challenges and have a bias toward action in the face of change, you will be empowered to make a real impact here, with a sharp and passionate team at your back. If Braze sounds like a place where you can thrive, we can’t wait to meet you.
WHAT YOU'LL DO
Braze is seeking a Staff Machine Learning Engineer to join our Predictive and Generative AI (PGAI) team. The team's mission is to deliver a truly engaging and personalized customer experience through the creation of ML and AI enhanced marketing solutions. We own those solutions end to end, from the models and the flexible training pipelines that build them for each customer to the high-throughput APIs that serve predictions into our messaging systems. You will help set the scope of what is possible for customer engagement at scale, and from that space of possibilities you will lead solutions from prototype to product and build the ML platform that runs them.
As the Staff Engineer on the team, you will:
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
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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
WHO YOU ARE
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
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
- Bonus:
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
Experience in our stack (Python, Ruby on Rails, MongoDB, Redis, Kubernetes)
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 (OTE) between $204,000 and $332,400/year (including bonus or commission). Your exact offer may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition to cash compensation, this role qualifies for a comprehensive Total Rewards package that includes equity grants of restricted stock (RSUs) so that you will own a piece of our company.
WHAT WE OFFER
Braze benefits vary by location, and we encourage you to review our specific benefits offerings for each country here. More details on benefits plans will be provided if you receive an offer of employment.
From offering comprehensive benefits to fostering hybrid ways of working, we’ve got you covered so you can prioritize work-life harmony. Braze offers benefits such as:
Competitive compensation that may include equity
Retirement and Employee Stock Purchase Plans
Flexible paid time off
Comprehensive benefit plans covering medical, dental, vision, life, and disability
Family services that include fertility benefits and equal paid parental leave
Professional development supported by formal career pathing, learning platforms, and a yearly learning stipend
A curated in-office employee experience, designed to foster community, team connections, and innovation
Opportunities to give back to your community, including an annual company-wide Volunteer Week and donation matching
Employee Resource Groups that provide supportive communities within Braze
Collaborative, transparent, and fun culture recognized as a Great Place to Work®
ABOUT BRAZE
Braze is the leading customer engagement platform that empowers brands to Be Absolutely Engaging™. Braze helps brands deliver great customer experiences that drive value both for consumers and for their businesses. Built on a foundation of composable intelligence, BrazeAI™ allows marketers to combine and activate AI agents, models, and features at every touchpoint throughout the Braze Customer Engagement Platform for smarter, faster, and more meaningful customer engagement. From cross-channel messaging and journey orchestration to Al-powered decisioning and optimization, Braze enables companies to turn action into interaction through autonomous, 1:1 personalized experiences.
The company has been consistently recognized as a Leader in marketing technology by industry analysts, and was named a G2 “Best of Marketing and Digital Advertising Software Product” in 2026. Braze was also named a 2026 Best Places to Work by Built In, a 2025 America’s Greenest Companies by Newsweek, and a 2025 Fortune Best Workplace in Technology™ by Great Place To Work®. Braze is also proudly certified as a Great Place to Work® in the U.S., the UK, Australia, and Singapore.
The company is headquartered in New York with offices in Austin, Berlin, Bucharest, Chicago, Dubai, Jakarta, London, Paris, San Francisco, São Paulo, Singapore, Seoul, Sydney and Tokyo.
BRAZE IS AN EQUAL OPPORTUNITY EMPLOYER
At Braze, we strive to create equitable growth and opportunities inside and outside the organization.
Building meaningful connections is at the heart of everything we do, and that includes our recruiting practices. We're committed to offering all candidates a fair, accessible, and inclusive experience – regardless of age, color, disability, gender identity, marital status, maternity, national origin, pregnancy, race, religion, sex, sexual orientation, or status as a protected veteran. When applying and interviewing with Braze, we want you to feel comfortable showcasing what makes you you.
We know that sometimes different circumstances can lead talented people to hesitate to apply for a role unless they meet 100% of the criteria. If this sounds familiar, we encourage you to apply, as we’d love to meet you.
OUR AI-POWERED BRAZE RECRUITMENT PROCESS
At Braze, we’re committed to a fair and transparent candidate experience. To help our recruitment teams focus on what matters most — the person behind each application — we use AI-assisted tools at certain stages of our recruitment process.
This includes using AI to analyze the experience, skills and qualifications in your application materials to help with screening and prioritizing candidates. Such screening may amount to a form of solely automated decision-making. We also use AI for administrative support, like scheduling and recording interviews and summarizing interview notes. Our recruiting teams remain responsible for all hiring decisions and are involved throughout the process.
Depending on where you are located, you may have the right to request further information about how AI is used in our recruitment process, to opt out of AI-assisted review, to request a manual review of any decision made or to contest a decision.
Please contact us at talentdata.privacy@braze.com for any requests or questions. To find out more about our hiring process, check out this page.
Notice Regarding Automated Employment Decision Tool (NYC Local Law 144)
Our use of AI during the application review process may include the use of automated employment decision tools. Pursuant to New York City Local Law 144, for roles based in New York City, or if you reside in New York City, you have the right to request an alternative selection process or a reasonable accommodation instead of AI-assisted review. Please submit any such request to our Talent Acquisition team at talentdata.privacy@braze.com promptly after applying. A summary of the most recent bias audit results for such tool is available here.
Please see our Candidate Privacy Policy for more information on how Braze processes your personal information during the recruitment process and, if applicable based on your location, how you can exercise any privacy rights.
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