Customer Success Automation Engineer

The opportunity

We're hiring a Customer Success Engineer to sit at the intersection of AI innovation and customer operations. This isn't a traditional engineering role — it's a mission-critical position dedicated to one thing: making our Customer Success team dramatically more effective through the power of AI. Our mission is to make financial advice more affordable and accessible — and that depends on a Customer Success function that scales through intelligence, not headcount. You'll spend the majority of your time designing and shipping AI agents, automations, and internal tools — with a product manager's instinct for what's worth building and a CX obsession for how it feels to use. That means building intelligent onboarding flows, self-service support experiences, and proactive engagement tools that users interact with every day. This role is for someone who is deeply technical, product-minded, and moves fast in an Agile environment. You think like an engineer, an operator, and a PM all at once — opinionated about tooling, obsessive about customer experience, and pragmatic about the tradeoffs that keep our team lean and our mission within reach.

*This is hybrid role, requiring three days per week in our San Francisco, Culver City, or Dallas office.

Your impact

• Design, build, and iterate on AI agents and applications — both internal tools that automate workflows for the Customer Success team and customer-facing experiences that optimize key moments in the journey (onboarding, self-service support, proactive engagement).
• Scope and prioritize what to build — look at a messy list of requests and identify the highest-leverage opportunity rather than just building what's asked.
• Think from the customer's perspective first — whether building internal tools or customer-facing experiences, consider how real people will interact with what you build and iterate based on their feedback.
• Define success before shipping — set clear metrics, track adoption and outcomes, and use data to validate whether a tool is actually moving the needle.
• Understand the business context behind the work — reason about cost-to-serve, efficiency tradeoffs, and ROI, and use that lens to make better decisions about what to build and how.
• Own the technical decisions end-to-end — select the right LLMs, orchestration frameworks, and third-party tools for each use case, and write clean, maintainable code (leveraging AI-assisted development) that ships quickly without sacrificing reliability.
• Partner with DevOps and Security to ensure AI agents and applications are deployed reliably, meet security and compliance standards, and are built with production-grade infrastructure.
• Collaborate closely with CS leadership and frontline team members to deeply understand pain points and translate them into high-impact solutions.
• Drive adoption and scalability — run demos and training sessions to onboard users onto new tools, and establish best practices, playbooks, and documentation so solutions stick.

What you bring

• 5–8+ years software engineering background with production experience building and shipping systems.
• A proven track record of owning outcomes, not just outputs — someone who prioritizes ruthlessly, holds themselves accountable to results, and doesn't consider something shipped until it's working in the hands of users.
• Strong product instincts — comfortable looking at a messy list of requests and identifying the highest-leverage opportunity, defining what "done" means, and saying no to work that won't move the needle.
• Genuine obsession with customer experience — you think about how real people (advisors, internal teams) will interact with what you build, and you iterate based on their feedback rather than assumptions.
• Comfort reasoning about cost-to-serve, efficiency tradeoffs, and ROI — you make decisions about what to build and how based on business impact, not just technical interest.
• Strong proficiency in at least one modern language (Python preferred), comfort picking up new frameworks quickly, and fluency with AI coding assistants — using them to ship more, faster, without sacrificing reliability or code quality.
• Hands-on experience building with LLMs (OpenAI, Anthropic, Gemini, open-source), agent frameworks (LangChain, LlamaIndex, CrewAI, or similar), and a strong intuition for prompt engineering, RAG architectures, and the practical limits of AI in production — including how to evaluate tools and models on cost, latency, accuracy, and maintainability tradeoffs.
• Deep comfort with APIs, webhooks, and integration patterns, plus experience working with messy real-world data and transforming it into clean, reliable pipelines.
• Comfortable working with CRM platforms (we use Salesforce, including Service Cloud and Experience Cloud) or confident ramping up quickly. Prior Salesforce experience (Apex, Flow, or Salesforce APIs) is a strong bonus.
• Self-directed and proactive — you identify opportunities, propose solutions, and drive them to completion without heavy oversight, even through initial ambiguity.