Staff AI Product Builder

Role Description

In this role, you’ll own AI-powered improvements in core brightwheel workflows end-to-end, from opportunity sizing to launch to iteration. You’ll ship experiences that make administrators and teachers faster and more effective, while creating shared patterns and infrastructure that enable the broader engineering team to build safely and consistently.

• Design and build cross-cutting AI services (such as retrieval, context, evaluation, and guardrails) that power multiple product areas like classroom workflows, billing, and family communication.
• Own the end-to-end product loop as a hybrid PM+Eng+Data builder: talk to customers and internal teams, define success metrics, design workflows and user experiences, shape data and evaluation plans, and ship iterative releases from prototype to reliable, scalable production.
• Create shared abstractions and tooling for AI — including common prompt and tool patterns, logging and monitoring, and reusable components — so other engineers can build on a consistent foundation.
• Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations, and recommend what should happen next — not just report what happened.
• Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features.

Qualifications

• 5+ years of professional software engineering experience, with clear ownership of medium-to-large production systems from problem statement and design doc through launch and iteration.
• A proven track record of shipping AI-powered products to production, with concrete examples where LLMs meaningfully improved metrics like engagement, time saved, satisfaction, or retention.
• Hands-on experience with large language models (LLMs) in real applications, including prompt and tool design, retrieval-style patterns (such as RAG), and evaluation and monitoring in production.
• Strong computer science fundamentals (e.g., data structures, algorithms, and systems design) and a generalist mindset, comfortable moving between backend, data, and UX to get the job done.
• Backend engineering skills in at least one modern web stack (such as Ruby on Rails, Python, Go, or Node), plus confidence with relational databases and larger datasets, from data modeling to performant queries and analytics.
• Experience building modern web front-ends, ideally with React or a similar component-based framework.

Requirements

• Driven by outcomes.
• AI-native.
• A product-driving technical leader.
• Full stack with a platform mindset.
• Thoughtful about AI’s limits.
• Security-minded.

Nice-to-haves

• Formal training in computer science (4-year CS degree or equivalent depth in core CS topics).
• A portfolio of personal AI projects, open-source work, or writing that shows how you think about applied AI in real-world settings.
• Background in vertical SaaS, ecommerce, or other operations-heavy domains.
• Experience designing shared platforms or frameworks (for example, internal SDKs, evaluation services, or experimentation tooling) adopted by multiple teams.
• A track record of raising the bar for quality and operations: writing secure, testable, maintainable code; automating and simplifying dev/test/ops workflows; writing and reviewing design docs; mentoring other engineers; and contributing to hiring through interviews and feedback.

Technology

• Backend: Ruby on Rails, Sidekiq
• Data: PostgreSQL on Amazon RDS, Redis, and event and analytics pipelines
• Frontend: React with TypeScript and Emotion
• Mobile: Native iOS (Swift) and Android (Kotlin with Jetpack Compose)
• Cloud & Infrastructure: Docker, Kubernetes on Amazon EKS, GitHub Actions and FluxCD for CI/CD, and AWS services such as S3, CloudFront, CloudWatch, and SNS
• AI & Automation: AWS Bedrock and other hosted large language models, vector search, orchestration and agent frameworks, and modern AI coding tools like Cursor