Principal Artificial Intelligence Software Engineer (GTM)

Our team is passionate, talented, and deeply customer-focused. We build the platform that thousands of early education programs rely on every day to run their business, serve families, and support children’s growth

As a vertical SaaS company, brightwheel already sits in the middle of all the key workflows in early education: enrollment, billing, staffing, classroom planning, and family communication

We are now extending that foundation into a system of action – where software not only records what happened, but also anticipates work, recommends next steps, and takes safe, automated action on behalf of school leaders, teachers, families, and our internal teams

We’re looking for an AI-native software engineer to join our GTM engineering team

This role is for someone who wants to make a direct impact upon the bottom line

You will design and build AI solutions to solve the challenges internal teams wrestle with on a daily basis. You will build products that allow our go-to-market team to operate at peak performance–delivering the right information at the right time

You will build solutions that allow AI agents to take action on operational data, accelerating the operations of internal teams

You will lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring other engineers, and raising the bar for how we design, ship, and operate AI-powered features

They prototype quickly, validate assumptions with users or internal teams, define success metrics, and iterate in production. They use AI agents, coding tools, scripts, and automation to move faster, while raising the bar for quality, reliability, privacy, security, and customer trust

Own meaningful problems from discovery through launch, measurement, and iteration

Use AI to improve both how you build and what brightwheel can deliver

Prototype quickly, validate assumptions, and use working software to create clarity

Raise the bar for quality, reliability, security, privacy, observability, and execution

Design and build cross-cutting AI services (such as retrieval, context, evaluation, and guardrails) that power go-to-market operational workflows, optimizing the efficacy of internal teams

As a hybrid PM+Eng+Data builder: own the end-to-end product loop for the problems you take on: talk to customers and internal teams, define the success metric, design the workflow and user experience, shape the data and evaluation plan, and ship iterative releases from prototype to reliable, scalable production

Create shared abstractions and tooling for AI – for example, 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

Technology:

AI and Automation: AWS Bedrock and Bedrock AgentCore, other hosted large language models, LangChain and LangGraph agent orchestration, vector and semantic search, LangFuse, and modern AI coding tools like Cursor, Claude Code, and Codex

Backend: Python with FastAPI, async workers and queue-driven jobs

Data: PostgreSQL on Amazon RDS, Amazon Redshift, Redis, DynamoDB, and large-scale entity resolution, enrichment, and ingestion pipelines

Frontend: React with TypeScript, TanStack Query, Vitest and Cypress

Data Engineering and Integrations: Airflow, headless Chrome, S3-backed data lakes, and deep Salesforce integration alongside other GTM systems

Cloud and Infrastructure: Docker, Kubernetes on Amazon EKS, ECS Fargate, AWS CDK and CloudFormation, GitHub Actions and FluxCD for CI/CD, and AWS services such as S3, CloudFront, Lambda, SQS, and CloudWatch- The best candidates do not wait for perfect requirements

Can explain how you have increased your own velocity without lowering your quality bar

Care deeply about privacy, security, reliability, and customer trust

Communicate clearly and work well across functions

Have owned production work from problem definition through launch and iteration

Have strong engineering fundamentals and can reason across systems, data, APIs, product surfaces, and infrastructure

Have examples of automating your own work, your team’s work, or broader company workflows

Use AI tools and agents as a real part of your engineering workflow, not as a novelty

Are comfortable operating in ambiguity and turning unclear problems into shipped software

Have 8+ years of professional software engineering experience

Experience shipping AI-powered products, workflows, or internal tools to production

Experience with retrieval, tool use, evaluation, monitoring, orchestration, or agent workflows

Experience in vertical SaaS, education, fintech, healthcare, CRM, ecommerce, or another operations-heavy domain

A portfolio of personal projects, internal tools, open-source work, writing, demos, or side projects that shows builder energy and taste

Experience building AI powered systems

Experience collaborating with internal customers; not only building out what is asked, but building out novel solutions that amplify those customers’ capabilities (thinking creatively to meet business objectives)