Senior Software Engineer II

Invoca is an AI-powered revenue execution platform that brings together marketing, commerce, and contact center teams to turn every customer interaction into measurable, profitable growth. Join our dynamic, fast-growing team, where innovation and collaboration are at the core of our culture.

This role is on our Integrations team, where we connect Invoca’s AI agents to the systems our customers already run — CRMs, ad platforms, contact centers, healthcare systems, and the web — so an agent can find a lead, book an appointment, or act in the customer’s own system. We own that surface end to end: vendor connectors and MCP servers, webhooks, Google Ads, the web widget, and the production monitoring behind them.

We are making those integrations self-serve and reliable enough to trust. That includes Google Local Services Ads, customer MCP connectors with evaluations, event ingestion, web actions for voice and SMS, and voice agents in the customer’s contact center. You will work in Ruby on Rails and Type Script, share on-call for real customer traffic, and use AI agents to write and review the code.

You decide what a human has to check before it reaches a customer.

This role is not defined by a single language or framework. It is defined by outcomes: the speed, quality, and trust with which this team ships. You own meaningful pieces of the team’s roadmap, you make the engineering system around you faster and safer over time, and you treat AI as part of how the work gets done rather than a tool you occasionally reach for.

• Own a project, or a major piece of one, from scoping through shipping, and confirm afterward that it achieved the outcome it was meant to produce.

• Recognize where your work touches other systems and teams, and resolve those dependencies and conflicts rather than handing them off.

• Define what good looks like for the work you own and hold that bar, rather than waiting for it to be defined for you.

• Own technical design for a feature or service. Decide as clearly what to leave out as what to build, and back your choices with a working prototype.

• Weigh testability, scalability, and maintainability without being prompted, and favor the simplest approach that will hold up.

• Use AI agents as a routine part of writing production code: run several in parallel, get productive quickly in unfamiliar parts of the codebase, and review AI output efficiently by focusing on the risky parts.

• Build your own skills, agents, or tooling when it would make the team faster, and improve the parts of our shared development system your work depends on.

• Know where it is safe to let AI run and where a human must review before anything reaches production, and set that bar for the work you own.

• Build customer-facing features on top of AI systems: handle non-deterministic outputs, design and run evaluations, manage latency, and debug unexpected model behavior.

• Establish safe, observable, and auditable ways to bring AI into the product, with monitoring and tracing in place so the team can see how agents behave with real users.

• Understand the customer’s real problem before building starts, and confirm afterward that it was solved.

• Take part directly in customer feedback sessions, and use product and adoption metrics to guide what you build next.

• Partner with your Product Manager to prioritize based on measurable customer and business impact, not technical elegance alone.

• Troubleshoot independently across the team’s services, contribute to post-incident reviews, and recommend process and monitoring fixes alongside code fixes.

• Spend review time where the risk is…