AI Platform Engineer II

<div class="content-intro"><p>At Braze, we have found our people. We’re a genuinely approachable, exceptionally kind, and intensely passionate crew.</p> <p>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.</p> <p>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.</p> <p>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.</p> <p>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.</p></div><h4>WHAT YOU'LL DO</h4> <p>Join the AI Platform team at Braze to build and scale BrazeAI Decisioning Studio - a reinforcement learning platform at the forefront of AI Decisioning. The platform runs continuous experimentation and personalizes customer engagement at the individual level, helping brands move from rule-based campaigns to autonomous, self-optimizing interactions. You'll work at the intersection of cloud-native infrastructure, data-intensive systems, and machine learning in production.</p> <p>Main responsibilities:</p> <ul> <li>Build and maintain critical services and subsystems on our AI platform, balancing performance with cost-effective operations</li> <li>Implement cloud-native solutions that ensure reliability, scalability, and fault tolerance</li> <li>Troubleshoot production incidents end-to-end, going deep to identify root causes and implement durable fixes</li> <li>Contribute to observability practices using Sentry and Datadog to proactively detect issues and minimize downtime</li> <li>Collaborate with data scientists, ML engineers, and product teams to translate real-world use cases into platform capabilities</li> <li>Improve developer experience by streamlining workflows, enhancing tooling, and supporting MLOps best practices</li> </ul> <p>Tech stack:</p> <ul> <li>Core Data ML: Python, Ibis, FastAPI, Dataproc (Spark), SQL, BigQuery, MLflow, Streamlit</li> <li>Platform Infrastructure: Google Cloud Platform, AWS, Kubernetes, Helm, Terraform</li> <li>Workflows Orchestration: Airflow, RabbitMQ, Celery</li> <li>CI/CD: GitHub Actions, Jenkins</li> <li>Observability: Sentry, Datadog</li> </ul> <p>Why this role:</p> <ul> <li>Production ML at scale: no toy datasets or notebook demos; you’re building infrastructure that powers real AI workloads</li> <li>Engineering rigor: unit and integration tests, modular design, CI/CD, pair programming, and code reviews are how we work, not aspirations</li> <li>Learn continuously: deep exposure to ML system architecture, end-to-end ML workflows, and reinforcement learning systems</li> </ul> <h4>WHO YOU ARE</h4> <ul> <li>2-4 years of experience in platform engineering, infrastructure, or a related backend role</li> <li>Solid understanding of platform architecture, particularly in ML or data-intensive environments</li> <li>Hands-on experience with Kubernetes and cloud infrastructure (GCP preferred)</li> <li>Ability to troubleshoot complex distributed systems under pressure</li> <li>Writes clean, modular code with a focus on testable APIs and maintainable design</li> <li>Experience working with AI coding assistants. Understands effective prompting strategies and can articulate when these tools add value versus when they're not appropriate</li> <li>Clear communicator who can work across technical and non-technical stakeholders</li> <li>Proactive problem solver who identifies is ... (truncated, view full listing at source)