About this role
Engineers who thrive on tackling complex infrastructure challenges and enjoy driving large-scale production systems will find a natural home in this role. Braze is looking for someone to anchor the Predictive and Generative AI team by taking ownership of the underlying machine learning platform and ensuring smooth, high-velocity deployments.
The team builds production-grade marketing solutions that deliver personalized customer experiences at global scale, handling everything from distributed training pipelines to high-throughput prediction APIs. In this hands-on delivery capacity, the chosen candidate will direct architectural initiatives, lead incident response, oversee deployment tooling, and collaborate across technical boundaries to keep operations fast, safe, and efficient.
What you will do
- Identify and drive transformative initiatives that change how the team runs machine learning in production.
- Build and ship complex infrastructure initiatives yourself from the design phase through production deployment.
- Own the technical vision and production quality bar for model training, deployment, serving, and observation.
- Drive initiatives that span multiple teams by coordinating on shared infrastructure, deployment tooling, and data systems.
- Raise overall engineering quality through rigorous design reviews, code reviews, and production readiness checks.
- Mentor other senior engineers and data scientists while connecting technical decisions to broader business outcomes.
What you need
- Bring 8+ years of experience building and operating distributed systems in production with a strong focus on deployment and operations.
- Demonstrate hands-on experience with production machine learning workloads such as training pipelines, model serving, or feature systems.
- Act as a technical leader who has owned direction for a team and guided multi-quarter initiatives across team boundaries.
- Possess deep working knowledge of Kubernetes and cloud infrastructure, including identity, networking, and cost management.
- Communicate effectively through verbal and written channels to build consensus and guide decision-making.
Nice to have
- Queueing and orchestration systems such as Celery, RabbitMQ, Kafka, or Ray
- ML platform tooling including MLflow, model registries, feature stores, or observability platforms
- Experience in the company technology stack covering Python, Ruby on Rails, MongoDB, Redis, and Kubernetes
- Operating under compliance regimes like SOX or HIPAA
- Customer engagement, personalization, or marketing technology domain experience
Benefits
- Competitive compensation that may include equity grants of restricted stock
- Retirement and Employee Stock Purchase Plans
- Flexible paid time off
- Comprehensive medical, dental, vision, life, and disability benefit plans
- Family services including fertility benefits and equal paid parental leave
- Professional development support through career pathing, learning platforms, and a yearly learning stipend
Skills and tech stack
Questions candidates ask
What is the pay range for this position?
For candidates based in the United States, the starting pay range is expected to be between $184,000 and $314,000 per year. Expected On Target Earnings range between $204,000 and $348,000 per year including bonus or commission.
What are the primary responsibilities for this engineering role?
You will own the platform underneath and make deploying, operating, and scaling machine learning fast, safe, and efficient. This includes carrying complex infrastructure initiatives from design through production and setting the technical vision for model training and serving.
What kind of background is required for applicants?
Applicants need 8+ years building and operating distributed systems in production with depth in deployment and operations. Hands-on experience with production machine learning workloads and deep knowledge of Kubernetes and cloud infrastructure are also required.