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Salary: USD 184,000 - 299,812 / annual
Braze is seeking a Staff Software Engineer to join the AI Decisioning Experience team, playing a pivotal role in shaping the future of reinforcement learning-based personalization solutions. This is a full-stack role spanning product strategy, architecture, and hands-on engineering.
Key responsibilities include leading high-impact initiatives across Decisioning Studio (both self-serve DS Go and managed DS Pro tiers), collaborating with Product, Design, and ML engineering teams to translate a fast-moving AI roadmap into reliable software. You will build the complete stack—from dashboard UI to APIs and services connecting Braze's platform to ML pipelines—while leading efforts in web application architecture for scalability and performance.
You will influence Braze's product strategy through technical insights, mentor engineers on technical growth, and support teams implementing AI solutions for customer success. The role requires 7+ years of relevant experience with a strong track record of delivery. You must be comfortable across the full stack: designing data models, APIs, and background jobs while owning product surfaces end-to-end.
Required skills include strong frontend development (TypeScript, React; Vue is a plus), expertise in scalable web application architecture, and proficiency as an agentic development practitioner—AI coding agents should be your default workflow with a track record of dramatically above-baseline throughput without sacrificing quality. You must communicate clearly in writing and verbally, produce design docs that people read, and explain complex systems to engineers, product partners, and stakeholders.
Strongly preferred: experience developing UX for machine learning and data-intensive applications, building product surfaces on top of LLMs and agentic systems (embeddings, agent orchestration, evaluation), and proficiency with Python and/or Ruby on Rails. You should take pride in elegant, maintainable code, foster a culture of quality, navigate large codebases with multiple stakeholders, and have a proven track record mentoring engineers.
