AI Engineer

Position: Staff AI Engineer
About Spot On

We're not just building restaurant tech, we're giving independent restaurants the tools to compete and win. From our award-winning point-of-sale to AI-powered profit tools, everything we do helps operators boost profit, work smarter, and keep their best people. And every solution is backed by real humans who actually give a sh
* t about helping restaurants succeed.

* Named the #1 Restaurant POS by G2 (Fall 2025), based on ratings from real users

* Rated the top-rated point-of-sale (POS) for restaurants, bars, retail, and small businesses by Capterra users

* Awarded Great Places to Work and Built In's Best Workplaces for multiple years running

We move fast, care hard, and fight for independent restaurant operators to do what they love, and love doing it. If you're looking to make an impact with heart and hustle, Spot On is the place for you.

About the Accelerate Team

The Accelerate team is a new 5-person engineering team with a singular mission: prove that AI-native engineering practices can fundamentally change how a fintech engineering org builds software - then make that the new standard. The team has direct VP sponsorship, mission & charter, and three defined projects with hard deadlines.

We're hiring a Staff/Principal IC who has already demonstrated AI-assisted engineering s is not an "AI-curious" role. You write code with AI every day. You've built agents that do real work. You have strong, experience-backed opinions on which models to use for what, where agents fail, and how to build guardrails that actually hold. You've seen what works in production and you're impatient to do it at scale.

You will be the most technically senior AI practitioner on this team - the person everyone looks to for "how do we actually build this." You don't manage the team. You drive the architecture, build the hardest parts, and set the technical standard.

The Role:

Technical Lead, Not Manager

This isn't a "set up some CI/CD and plug in Copilot" role. You are the person who architects the systems that change how Spot On engineers build software. You will:

Be the AI engineering technical authority. You set the technical standard for how AI is used in code generation, review, testing, and task automation. You don't just implement tools - you design the systems, workflows, and guardrails that make AI-assisted development safe, measurable, and scalable in a PCI-regulated payments environment. The team lead sets priorities; you set the technical direction.

Drive the architecture and implementation of three concurrent projects:

* Automated PR Review - A risk-tiered AI review pipeline where AI handles first-pass review on every PR, with an escalation framework you design that determines when human review is mandatory (payment logic, PCI-scoped code, compliance paths) versus when AI approval is sufficient. You define the criteria. You tune the thresholds. You prove it works.

* Android and API Test Automation - A fully automated test harness for our Android-native POS and KDS products: virtual device provisioning, test execution, teardown, AI-assisted failure analysis - replacing multi-day manual QA cycles with sub-30-minute automated runs.

* AI Agent Swarms - A multi-agent pipeline that takes a PRD and produces a tested, reviewed pull request for tier 1-3 engineering work (nits, bugs, small features). You design the agent orchestration - planner, coder, tester, reviewer - and the blast-radius controls that keep it safe in fintech.

Evangelize by shipping, not by presenting. Your most persuasive argument is a working system, not a slide deck. You demo to engineering leads every two weeks with real metrics. You turn skeptics into adopters by showing them their own time savings.

Measure everything. You define the metrics framework: developer hours recovered, cycle time compression, quality ratios, cost per deployment. You report to the CTO monthly with quantified value delivered. If something isn't working, the data tells you before anyone else.

What We Require

This is a high bar intentionally. We need someone who has already done this, not someone who wants to learn.

Required experience:

* 5+ years building developer tools, infrastructure, or platform engineering systems - you understand how engineering orgs actually work at scale, not just how AI demos work

* 2+ years working deeply with LLMs and AI agents in production - not side projects, not hackathons. Production systems handling real code, real reviews, or real task automation. You can speak to failure modes, cost optimization, prompt engineering patterns, and model selection trade-offs from experience.

* Designed and operated AI-assisted code generation or review systems serving 50+ engineers or 30+ repositories. You know the difference between "cool demo" and "trusted by the team."

* Built multi-agent or agentic workflows - task decomposition, agent specialization, orchestration, error recovery. You've worked with Claude Code, OpenAI Assistants, Lang Graph, CrewAI, Auto Gen, or custom…