Senior Customer Success Engineer

The opportunity

We're hiring a Senior Customer Success Engineer to sit at the intersection of AI innovation and customer operations. This is not a traditional engineering role – it is a mission‑critical position dedicated to 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 will spend the majority of your time designing and shipping AI agents, automations, and internal tools with a product manager’s instinct for what is 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.

Hybrid role requiring three days per week in our San Francisco, Culver City, or Dallas office.

• 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 – assess a messy list of requests and identify the highest‑leverage opportunity rather than simply delivering what is 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 actually moves 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 that ships quickly without sacrificing reliability.
• Partner with Dev Ops and Security – 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 – 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.

• 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 is 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,…