Applied AI Architect

Braze is building an Applied AI function for its GTM organization to help pioneer how modern revenue teams operate with AI. This is a chance to work at the frontier of AI-enabled GTM execution at a scaled global SaaS company with sophisticated enterprise customers, advanced buyers, and a large customer-facing organization.

The team encodes the judgment of its best practitioners into systems that extend it across the entire revenue org, creates feedback loops that surface where execution can improve, and builds the AI-powered systems that make every GTM team member more effective at the moments that matter most.

This team is field-attached, product-minded, and outcome-driven. We treat GTM practitioners as users and their workflows as product surfaces. We build agents, intelligence layers, automations, and decision-support tools, then tune and refine them based on adoption, output quality, field feedback, and business impact. The goal is practical AI that earns trust in live GTM workflows and creates durable value at scale.

As an Applied AI Architect, you will own a defined surface area across the revenue lifecycle and use AI to transform how Braze executes within it. You'll operate across three modes simultaneously: as the practitioner whose field judgment (in part) the agents in your domain are modeled after, as the builder who designs and refines those agents on top of shared infrastructure, and as the observer who determines whether the outputs are actually changing how GTM teams engage with customers and prospects.

Your success is measured by whether practitioners adopt the outputs, whether those outputs improve behavior at critical moments, and whether that behavioral change produces measurable revenue and customer impact.

• →Conduct stakeholder research across the GTM motion, from pre-sales through post-sales, to map where intelligence gaps, workflow friction, and handoff failures are most acute. Translate findings into a structured and prioritized backlog of problems to solve, including problem statements, impact, and feasibility scoring, dependencies, and stakeholders.
• →Build and refine AI agents tailored to your area of the revenue lifecycle, contributing directly to system architecture, retrieval logic, and output calibration on top of shared infrastructure. Ship working solutions against real workflows
• →Stay embedded in regional operating rhythms: pipeline reviews, deal cycles, QBRs, renewal planning, and account strategy sessions. The system gets better because the individuals building it never leave the commercial and customer motion. Field proximity is the operating discipline.
• →Own the reliability and quality of the systems you manage. Monitor adoption, diagnose output failures, and tune/iterate continuously. You are not handing off to an ops team. You operate what you ship.
• →Contribute to the shared knowledge hub by validating field signals, structuring deals and customer patterns, and ensuring that intelligence captured across the revenue lifecycle flows back in a form that produces better agent outputs.
• →Recognize patterns across your area and scale what works. A system built for one account scenario, customer segment, or deal stage should become reusable infrastructure for adjacent situations.
• →Partner with Growth Engineering, PMM, Solutions Consulting, Pricing, Customer Success, and leadership to ensure the systems you build are grounded in a cross-functional context and adopted by the people they serve.
• →Help shape what this function becomes as the technology and the role category mature. The playbook is being written in real time, and you will be one of the authors.

We're looking for people who have lived the revenue lifecycle and can translate that experience into AI systems that scale practitioner judgment beyond what any individual can. You are highly autonomous, technically minded, self-directed, and comfortable operating as a senior individual contributor in a new domain.

• You are a builder. You get excited by taking ideas from concept to reality. You have used AI to build agents, automations, or tools that changed how real work gets done. You can scope and prioritize solutions, contribute to system architecture and retrieval design, evaluate outputs for quality, and diagnose why something was missed at the system level rather than just the content level. You own what you help build, and you improve it based on what you observe.
• You carry GTM judgement. You have spent a meaningful amount of time in a revenue role. You know what a discovery call that misses the real objection sounds like. You understand how deals move, where they stall, how buying committees make decisions, and what makes the difference between a customer who renews and expands versus one who churns. You understand the daily reality of AEs, SCs, BDRs, sales directors, CSMs, and account managers, because you have either done their job or worked closely alongside them for years.
• You think like a product leader. When you identify a gap in the field, you scope the problem, define the user, map the workflow, and build the solution. You think in terms of adoption. You know the difference between shipping something and shipping something people actually use. You treat GTM practitioners as your users and their workflows as your product surface.
• You are a pattern recognizer and systems thinker. When you build something that works for one scenario, you immediately see how it applies across adjacent workflows, customer segments, or deal stages. You think in reusable components and scalable frameworks.
• You are field-attached by instinct. You would rather be embedded in pipeline reviews, deal cycles, and customer strategy sessions than building from a desk. You know that the quality of what you build depends on staying close enough to the revenue motion to understand whether it is actually useful.
• You are playing a long game. GTM is being rebuilt around AI, and the people who understand both the field and the technology will define what that looks like. This role is an opportunity to be on that team early, to develop the skills and track record that will matter most over the next decade, and to do it inside a company with the scale and sophistication to make the work real. You want to build something that lasts.

• 6+ years of professional experience in a GTM role with direct involvement in the commercial motion: Solutions Consulting, Solutions Architecture, Account Executive, Sales Leader, GTM strategy, or a comparable role at a B2B SaaS company
• Demonstrated, hands-on experience building AI-powered tools, agents, automations, or workflows that transformed real work processes, with concrete examples you can speak to in depth
• Technical fluency sufficient to engage credibly on integration architecture, evaluate agent outputs at the system level, and contribute directly to prompt design, retrieval logic, and data workflows (e.g., Python, APIs, integrations) without always requiring translation from an engineering counterpart
• Strong understanding of enterprise sales workflows, deal stages, and the daily operating reality of the GTM personas you will be building for: AEs, SCs, BDRs, sales leadership, CS, and business value teams
• Strong field credibility with experience in executive-level customer engagement, sufficient to walk into an opportunity or executive business review and have the room take the output seriously
• A track record of taking solutions from problem identification through field adoption, not just building and handing off
• Comfort working across multiple workstreams simultaneously, balancing field engagement with system build and continuous refinement
• Highly autonomous, self-directed, and comfortable operating as a senior individual contributor

• Product Management background or experience, either as a career transition into GTM or as a complement to a field role, with demonstrated ability to ship things people actually use
• Experience in Solutions Consulting, Solutions Engineering, or field architecture roles within enterprise SaaS
• Proficiency with AI development tools and platforms (e.g., Claude, Claude Code, Cursor, custom agent frameworks, API integrations, workflow automation tools)
• Familiarity with enterprise SaaS technology stacks (e.g., Salesforce, Gong, Slack, Snowflake)
• Experience building and scaling internal tools or systems that were adopted beyond your immediate team
• Analytical capability sufficient to evaluate deal data, diagnose output failures, and build data-driven recommendation systems

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.

The best GTM practitioners, whether they are closing new business, managing renewals, or driving expansion, operate partly on instincts and context that others don't always have access to. Those instincts are built over years of field exposure, thousands of customer interactions, and pattern recognition that compounds with experience.

The technology now exists to encode practitioner judgment into systems that deliver it at scale. But building those systems requires people who carry the judgment in the first place. An engineer without field depth builds a technically correct system that the field ignores. A field practitioner without built capability creates insight that doesn't scale. The person who is both is in a unique position, and that intersection is where this role sits.

Braze operates at a global scale, with sophisticated enterprise customers, advanced buyers, and a large customer-facing organization. The patterns being built here will become the standard across SaaS. The window to be part of building them from the ground up is open right now.

If you have lived in the commercial motion, already built with AI, and want to define the frontier of what this function looks like at a scaled enterprise company, we would like to talk.

For candidates based in the United States, the pay range for this position at the start of employment is expected to be between $123,000 and $184,000/year, with an expected On Target Earnings (OTE) between $145,000 and $217,000/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.

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