AI Success Engineer

Productboard•San Francisco, CA

23h•$92,500 - $122,600•Hybrid

About The Position

As an AI Success Engineer, you’ll help customers adopt AI-first product management by guiding them through Spark — showing them how AI can transform the way they discover, prioritize, build, and communicate. You’ll work directly with product and engineering teams to turn Spark from something they’ve heard about into something they use every day. You’ll manage your own book of business, lead activation sprints, and build the consultative instincts that make the difference between a customer who uses Spark and one who can’t imagine working without it. AI is rewriting how product teams work — and this role puts you at the front of that shift, helping real customers move from curiosity about AI to depending on it every day. You won’t just talk about AI-first product management; you’ll coach product and engineering leaders through it hands-on, using their own data and workflows. The skills you’ll sharpen here — AI fluency and prompt design, consultative solutioning, and translating technical capability into business outcomes — are exactly the skills that will define customer-facing technical careers for the next decade.

Requirements

Experience in customer success, solutions architecture, implementation consulting, or strategic advisory roles within B2B SaaS.
Direct experience managing a portfolio of accounts across the post-sales lifecycle — activation, adoption, and renewal support.
Comfortable leading conversations with Director and VP-level product leaders — skilled in objection handling and solutioning across varied customer contexts and types.
Solid product management knowledge — familiar with discovery methods, prioritization frameworks (RICE, Value vs. Effort), OKRs, and roadmap communication. You understand the workflows Spark is designed to improve.
1–2 years of hands-on AI experience in a professional context — not just personal productivity, but actively using AI to deliver customer outcomes, accelerate workflows, or build AI-augmented solutions. You understand prompt engineering fundamentals, can design basic multi-step AI workflows, and have coached others on effective AI usage.
Familiar with enterprise product management tools and integration ecosystems — Jira, Slack, Salesforce, and similar platforms.
Strong customer-facing communication and relationship-building skills — you adapt your approach based on the audience and build trust through reliability, follow-through, and genuine curiosity about the customer’s business.
Self-motivated and organized — you manage your book of business independently day-to-day, ask for help on the right things, and contribute positively to team culture.

Nice To Haves

Hands-on experience with Productboard or Spark.
Background as a product manager yourself, not only advising them.
Experience in a high-growth or startup B2B SaaS environment.

Responsibilities

Guide customers toward AI-first product management — help them see Productboard Spark not as an add-on but as a core part of how they work.
Demonstrate AI-augmented workflows using the customer’s own data and show how Spark accelerates discovery, prioritization, and roadmap communication.
Manage a book of accounts — build trusted relationships with product leaders, understand their goals, and ensure each account progresses through activation to sustained adoption within allocated service hours.
Deliver activation sprints within the standard playbook — run use case sprints guided by established best practices and templates, escalating when novel or high-risk situations arise.
Build early AI fluency with customers — configure Spark context so it reflects the customer’s domain, coach users on effective prompting, and follow up with prompt templates and hands-on support when Spark adoption stalls after the first Sprint.
Support the transition from activation to adoption — structure monthly touchpoints around meaningful signals (MAU Index, Spark usage depth), not vanity metrics, and identify when guidance versus additional hours versus escalation is the right call.
Partner with Account Executives and Renewals Managers — support AE-led business reviews with product health data, Spark usage metrics, and adoption summaries. Identify expansion signals and surface them to the AE for commercial qualification.
Contribute to internal improvement — share playbook improvements, document solutioning patterns, and support onboarding of new ASEs. Feed observations about customer friction points and process gaps back to the team.
Feed insight into our product direction — share customer patterns and Spark adoption challenges that help Product and Enablement teams improve the platform and our delivery model.