Forward Deployed AI Accelerator

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. Braze is building an internal AI Transformation function to change how every team at the company works. Not as a center of excellence that publishes best practices from a distance, but as a team of practitioners partnering directly with business units to make AI the default starting point for work. Within that team, Forward Deployed AI Accelerators rotate across the company's non-revenue functions — Marketing, Finance, People, Legal, Operations, and beyond — embedding with one function at a time to find the highest-value workflows, rebuild them around AI alongside the people who own them, and leave each team able to keep going without us. A sibling team, Applied AI Architects, GTM, applies the same craft permanently attached to specific stages of the revenue lifecycle; this role is the broad-coverage, rotational counterpart. We are product-minded and outcome-driven. We treat the people we serve as users and their workflows as product surfaces, we build on shared infrastructure, and we tune what we ship based on adoption, output quality, and business impact. Braze employees are already building agents that compress multi-day workflows into minutes and tools that transform processes like research, reporting, and operational escalations. This team exists to accelerate that impact and systematically scale it across Braze. WHAT YOU'LL DO As an Applied AI Architect, Core Business, you'll embed with a functional team or cross-functional cohort of approximately 15–25 people, learn their work deeply, and rebuild their highest-leverage workflows around AI alongside them. You'll operate across three modes: as the researcher who finds where the real friction and opportunity live, as the builder who designs and ships working agents on shared infrastructure, and as the coach who moves a team from its first contact with AI to self-sufficiency — and then rotates to the next function. Unlike your GTM counterparts, you are not permanently attached to one area. Your measure of success is durable business impact aligned to key financial and efficiency goals, adoption that persists after you rotate out, and cohorts that can build for themselves. Run enablement and discovery across your assigned functions. Lead enablement sessions and stakeholder research across the teams you cover (e.g., Marketing, Finance, People, Legal, Operations) to map where intelligence gaps, manual effort, workflow friction, and handoff failures are most acute. Translate findings into a structured, prioritized backlog of problems to solve — problem statements, impact, and feasibility scoring, dependencies, and stakeholders — and use it to decide where to dig in and rebuild the process first. Build alongside the team. Create custom tools, agents, automations, and prompts tailored to the highest-value workflows, contributing directly to system architecture, retrieval logic, and output calibration on top of shared infrastructure. Ship working solutions on real deliverables, not theoretical demos. Coach toward self-sufficiency. Move people through a progressive maturity model: from awareness to first win to regular AI integration to full workflow transformation to self-sufficiency. Meet people where they are, and teach them to build and iterate on their own tools over time. The goal is independence, not dependence on you. Own quality during the engagement, then hand up the durable pieces. Monitor adoption, diagnose output failures, and tune continuously while you're embedded. As the engagement matures, transition the cohort's load-bearing agents into the shared Platform layer so they run durably after you rotate out — you operate what you ship until it's productized, not before. Recognize patterns and scale what works. A tool built for one team should become reusable infrastructure for the next. Document every tool, playbook, and transformation pattern you create so the full team c