AI Platform Engineer II

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 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. Main responsibilities: Build and maintain critical services and subsystems on our AI platform, balancing performance with cost-effective operations Implement cloud-native solutions that ensure reliability, scalability, and fault tolerance Troubleshoot production incidents end-to-end, going deep to identify root causes and implement durable fixes Contribute to observability practices using Sentry and Datadog to proactively detect issues and minimize downtime Collaborate with data scientists, ML engineers, and product teams to translate real-world use cases into platform capabilities Improve developer experience by streamlining workflows, enhancing tooling, and supporting MLOps best practices Tech stack: Core Data ML: Python, Ibis, FastAPI, Dataproc (Spark), SQL, BigQuery, MLflow, Streamlit Platform Infrastructure: Google Cloud Platform, AWS, Kubernetes, Helm, Terraform Workflows Orchestration: Airflow, RabbitMQ, Celery CI/CD: GitHub Actions, Jenkins Observability: Sentry, Datadog Why this role: Production ML at scale: no toy datasets or notebook demos; you’re building infrastructure that powers real AI workloads Engineering rigor: unit and integration tests, modular design, CI/CD, pair programming, and code reviews are how we work, not aspirations Learn continuously: deep exposure to ML system architecture, end-to-end ML workflows, and reinforcement learning systems WHO YOU ARE 2-4 years of experience in platform engineering, infrastructure, or a related backend role Solid understanding of platform architecture, particularly in ML or data-intensive environments Hands-on experience with Kubernetes and cloud infrastructure (GCP preferred) Ability to troubleshoot complex distributed systems under pressure Writes clean, modular code with a focus on testable APIs and maintainable design Experience working with AI coding assistants. Understands effective prompting strategies and can articulate when these tools add value versus when they're not appropriate Clear communicator who can work across technical and non-technical stakeholders Proactive problem solver who identifies issues and works around obstacles without waiting for direction For candidates based in Ontario, the pay range for this position at the start of employment is expected to be between CA $125,280 and CA $235,736 /year with an expected On Target Earnings (OTE) between CA $139,200 and CA $258,000 /year (including ... (truncated, view full listing at source)