About this role.
Mercury seeks an AI Operations lead to embed with internal business functions, initially likely Compliance, and convert AI opportunities into measurable operational improvements. The role combines business operations, hands-on AI workflow development, and change management, with ownership from problem definition through adoption and handoff. The successful candidate will define KPIs, build AI-enabled automations and internal tools using existing engineering platforms, and iterate based on real outcomes. Strong stakeholder influence, technical fluency with APIs, SQL, and low-/no-code tools, and comfort operating amid ambiguity are central to success. This is a senior individual-contributor role that will also establish Mercury's operating model for AI Operations.
Role DNA
A quick view of the complexity, pace, ownership and collaboration implied by the job description.
Job Complexity
5/5Pace & Pressure
5/5Autonomy Level
5/5Communication Load
5/5Salary analysis
Estimated compensation compared with the broader US market for similar roles.
Core skills
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Sample interview questions
Use a structured example that explains how you mapped the workflow, distinguished the root constraint from surface-level inefficiencies, established a baseline KPI, implemented a solution, and measured the result. Emphasize adoption metrics and how you ensured the improvement persisted after launch.
I would begin with observation, stakeholder interviews, and workflow data to understand volumes, handoffs, decision points, exceptions, risk controls, and current service levels. I would jointly select a high-value, feasible problem, define success metrics and guardrails, prototype within the existing AI platform, then iterate with end users until the outcome and adoption are demonstrably improved.
I assess the process for repeatability, information availability, error tolerance, required human judgment, security and compliance requirements, integration feasibility, and expected value. I favor narrowly scoped workflows with clear evaluation criteria and human review where consequences or uncertainty are high.
I would explain the change in terms of the stakeholders' own pain points and success measures rather than the technology itself. I would involve representative users early, show evidence from a small pilot, provide training and documentation, address failure modes transparently, and establish a clear owner and feedback loop after rollout.
I am comfortable translating operational requirements into implementable workflows using APIs, SQL, low-code or no-code platforms, and AI-agent patterns. I can assess data quality, integrations, permissions, evaluation methods, and escalation paths, while partnering with engineering on platform-level reliability, security, and scalability.
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 experience building with AI: agents, workflows, automations, internal tools. Not just using AI products day to day.
- Be comfortable defining your own KPI, plan, and build in ambiguity, then defending all three to a skeptical team lead.
- Be fluent enough with tooling (APIs, low-code and no-code builders, SQL) to know what’s feasible, and fluent enough in plain language to sell it to a team that didn’t ask for a new tool.
*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC.
Mercury values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Mercury are considered without regard to race, color, religion, national origin, age, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, sexual orientation, or any other legally protected characteristic. We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities or special needs. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.
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Total Rewards
The total rewards package at Mercury includes base salary, equity (stock options/RSUs), and benefits.
Our salary and equity ranges are highly competitive within the SaaS and fintech industry and are updated regularly using the most reliable compensation survey data for our industry. New hire offers are made based on a candidate’s experience, expertise, geographic location, and internal pay equity relative to peers.
Our target new hire base salary ranges for this role are the following:
US employees in New York City, Los Angeles, Seattle, or the San Francisco Bay Area:
$180,200—$225,200 USD
US employees outside of New York City, Los Angeles, Seattle, or the San Francisco Bay Area:
$162,200—$202,700 USD
Canadian employees (any location):
$170,300—$212,800 CAD
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