About this role
Mercury is building the financial stack for startups. We're working to make complex financial workflows feel simple, thoughtful, and perhaps even a little magical. You can see some of this in action in our demo dashboard .
Most AI initiatives don't stall because the models aren't capable or the tools don't exist. They stall at the last mile: a promising solution gets built, but the way a team works never really changes. AI Operations exists to close that gap. We're looking for a forward-deployed lead who can embed deeply with teams across Mercury, understand how their work actually happens, and turn AI's potential into measurable improvements.
You'll spend a few months at a time partnering closely with a particular function. You'll learn the team's work well enough to identify the true constraint—not simply the most tedious task. Then you'll build against it using the tools and infrastructure created by Mercury's AI Engineering team, iterating until the outcome moves and the new way of working sticks. Once it does, you'll make sure the team can carry it forward. You'll leave behind well-built tools, clear documentation, and confident owners before moving on to the next high-leverage problem.
You'll be the first person to bring it to teams across the rest of Mercury, beginning most likely with Compliance. That means you won't just run the playbook—you'll help write it. You'll determine what makes an embed successful, how long it should last, what a durable handoff requires, and how AI Operations should decide where to focus next. This role sits somewhere between builder, operator, and change agent. You'll be responsible for helping teams adopt new workflows through thoughtful implementation, training, documentation, and plenty of iteration.
Mercury aims to make banking* feel secure, reliable, thoughtful, and perhaps even magical. Your job will be to make the way Mercury works internally feel just as considered.
You will:
• Embed with teams across Mercury and learn their work deeply enough to identify where AI could meaningfully improve a business outcome.
• Partner with functional leaders to define a clear measure of success before building—whether that means reducing turnaround time, shrinking a backlog, improving quality, or increasing capacity.
• Build and ship AI-powered workflows, automations, and internal tools using AI Engineering's existing platform.
• Test and iterate until the solution produces a measurable result, not simply until it reaches production.
• Lead the change management required to make new ways of working stick, including training, documentation, and ongoing support.
• Create durable handoffs, leaving each team with reliable tools, clear operating documentation, and an accountable owner.
You should:
• Have 4–7 years of experience in management consulting, business operations, product operations, or a similar role where you owned a business outcome end to end — not just a project plan.
• Have hands-on experienc