AI Product Builder

AI Product Builder

Motivosity is an employee recognition, rewards, and engagement platform. We help companies build cultures where people feel seen, connected, and appreciated — through peer-to-peer recognition, awards and milestones, spending accounts, manager tools, and eNPS surveys that track how engagement moves over time.

Our engineering mission is to delight customers through unparalleled speed, quality, and reliability. That commitment is concrete: rewards and recognition are delivered on time, to the right person, with the correct reward; every financial transaction is recorded and reconciled; and the platform is built to scale to companies of 100,000+ employees without disruption.

We're hiring an AI Product Builder to help us turn Motivosity into an AI-first platform — and to ship the practical AI features and internal tools that prove it.

Motivosity is putting more product ownership and decision-making into the hands of the people closest to the code, the customer problem, and the technical possibilities. This is not a narrow frontend, backend, ML, or prompt-engineering role. We're looking for someone who can own a full product workflow end-to-end and use AI to move faster, fill gaps, prototype quickly, and ship useful software.

The goal is not flashy demos. The goal is to ship software that saves time, improves quality, and creates measurable value for our customers and internal teams — in a recognition-and-rewards space where we intend to be the technology leader for AI.

• Own AI-powered product and internal workflow projects from start to finish.
• Turn ambiguous ideas into working software without needing every requirement defined upfront.
• Make product and technical decisions based on customer value, feasibility, risk, and speed of learning.
• Build across the full stack: frontend, backend, APIs, databases, integrations, AI workflows, tests, and deployment.
• Use AI tools as your default execution layer for design, coding, testing, debugging, documentation, and analysis — then apply your own judgment to review and approve the result.
• Build practical AI features using LLMs, structured outputs, retrieval, agents, and tool-calling where appropriate.
• Create simple evals and feedback loops to verify AI features actually work.
• Monitor usage, quality, cost, latency, errors, and user feedback after launch.
• Work directly with product, engineering, support, customer success, and leadership to find high-value problems.
• Ship small, useful things quickly and iterate based on real usage.

• Strong full-stack engineering experience and the ability to own production-quality code.
• Ability to independently own projects from discovery through production.
• Strong product judgment and the ability to make decisions without waiting for perfect requirements.
• Strong backend, frontend, database, and API skills.
• Experience building real software, not just prototypes.
• Practical experience using AI tools to accelerate engineering work.
• Hands-on experience with LLMs, agents, retrieval/RAG, structured outputs, or AI workflow automation.
• Comfort working directly with customers, internal users, PMs, designers, and business leaders.
• Strong debugging skills across the entire stack.
• Good judgment around security, privacy, permissions, and customer trust — especially important given we handle company budgets, employee wallets, and PII.
• Clear communication with both technical and non-technical teams.

If you're an engineer who wants to own real problems end-to-end, use AI as a force multiplier, and help define how AI shows up in the recognition space we'd love to talk!