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# Open Call for Founders / Founding Teams – Product Management and Engineering

AI Summary Mercury is seeking founders, founding teams, and highly entrepreneurial product-minded engineers to build and scale AI-driven financial products. This open call is not a fixed role: successful candidates will identify customer problems, rapidly prototype and ship solutions, and own measurable business outcomes. The work centers on Mercury Command, CLI, MCP, AI workflows, and experimentation with technologies such as RAG, evaluations, and agent harnesses. Candidates should have delivered a real product, tool, company, or open-source project and be comfortable operating across product management and engineering. The role emphasizes high autonomy, rapid iteration, customer proximity, and pragmatic decision-making.[Apply for this job](#job-application)[View company](https://jobicy.com/company/mercury.md)ShareRemote from[USA](https://jobicy.com/job-region/usa.md), [Canada](https://jobicy.com/job-region/canada.md)SalaryUndisclosedDepartment[Software Engineering](https://jobicy.com/categories/engineering.md)EmploymentFull TimeExperienceOpen levelPublished3 Nov 2026Apply before3 Dec 2026Listing views44Application actions1Application toolkit

## Make your next move.

Prepare your resume, explore your fit, and draft a cover letter for this opportunity.

Opportunity details

## About this role.

Open call for Founders / Founding Teams (Product Management / Engineering roles)

In 1973 at Xerox PARC, Bob Metcalfe sketched a way for many computers to share a single wire — borrowing the throughput math from his study of Hawaii’s ALOHAnet — and he and David Boggs built it and measured it until it held. Behind Ethernet was a two-person team that turned a contested idea into the measured standard now carrying almost all the world’s wired traffic.

At Mercury, we’re looking to accelerate several of our AI products that our customers love like Command, CLI, and MCP. We’re building for ambitious people of all kinds, and are a company made of founders and small technical teams trying to change the world – as part of that, we’re taking open applications for founders or small technical teams (Eng+) that want to work together on building the AI products of the future. This isn’t a fixed role. We expect the nature of work is changing, and small teams of people paired with AI product surface and autonomy to build can create 10x outcomes. Apply as a small team or as an individual

Here are some things you’ll do on the job:

* Find the problem. Work close enough to customers that you spot what matters before anyone assigns it to you — then decide it’s worth building.
* Build and automate the workflow end to end using Command, CLI, MCP, and the rest of our AI stack.
* Ship to customers fast — quick understanding, tight iteration, deploy.
* Measure the impact honestly, then do the next one. Kill what didn’t move; double down on what did.
* Run as an autonomous team — pick the target, ship the thing, own the outcome.

You should:

* Have shipped something real – a product, a tool, a company, or an open-source project people actually use.
* Be dangerous across the stack: PMs who can prototype, engineers with product taste.
* Have strong opinions about AI products and the judgment to recognize fast when one of them is wrong.
* Hands experience building AI products with AI, and knowledge about RAG, evals, agent harnesses a plus
* Move fast and measure. Expect most experiments to fail and treat that as the price of finding the ones that don’t.
* Care about craft where it’s impactful and resist polishing what isn’t.
* Communicate clearly and own outcomes – we want real business impact, customer time/pain saved, and a path to scaling what works

Learn more about what we’re building:

* [Mercury Command](https://mercury.com/command) — Complete financial work end-to-end with AI. Tell Mercury what to do in natural language; it stages the action and executes with your approval.
* [Mercury MCP & CLI](https://mercury.com/api) — Give your AI agents direct access to verified financial data, or work with your Mercury account straight from the terminal.
* [The bank of the future knows your business](https://mercury.com/blog/series-d-announcement) — Immad on what we’re building toward and why now is the moment.

What happens next?

Submit your application and our recruiting team will follow up. We’re genuinely open to connecting with founders and teams at any stage — whether you’re a strong fit for something we’re actively building toward right now, or this is the start of a longer relationship we pick back up when the opportunity is right. If you’re building, we want to know you.

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. As this is a general interest application, compensation ranges will be shared by our recruiting team if you move forward for a specific role.

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.

Show more

[Apply now >](https://jobicy.com/jobs/158675-open-call-for-founders-founding-teams-product-management-and-engineering.md)

>  Annual salary information is not provided for this position. Explore salary ranges for similar roles in our [Salary Directory ›](https://jobicy.com/salaries.md)

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## Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

### Job Complexity

5/5EasyHard

### Pace & Pressure

5/5RelaxedFast-paced

### Autonomy Level

5/5GuidedFull ownership

### Communication Load

4/5IndependentCollaborative

AI insightThis is a highly ambiguous, senior-level opportunity requiring applicants to independently find valuable problems and deliver production outcomes. Success depends on unusual breadth across AI engineering, product judgment, customer discovery, and business impact measurement.

## Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate$220,000US market range (per year)$180k–$300k0$330k

AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary range is disclosed. The yearly USD estimate is an AI-generated market estimate for a senior, founder-caliber product engineering role in the United States or Canada, inferred from the scope, autonomy, fintech context, and AI specialization.

## Core skills

Skills and capabilities most closely associated with this opportunity.

[AI product development](https://jobicy.com/jobs?search_keywords=AI%20product%20development.md)[Product strategy](https://jobicy.com/jobs?search_keywords=Product%20strategy.md)[Full-stack engineering](https://jobicy.com/jobs?search_keywords=Full-stack%20engineering.md)[Rapid prototyping](https://jobicy.com/jobs?search_keywords=Rapid%20prototyping.md)[Customer discovery](https://jobicy.com/jobs?search_keywords=Customer%20discovery.md)[RAG systems](https://jobicy.com/jobs?search_keywords=RAG%20systems.md)[AI evaluation](https://jobicy.com/jobs?search_keywords=AI%20evaluation.md)[Agent orchestration](https://jobicy.com/jobs?search_keywords=Agent%20orchestration.md)[Workflow automation](https://jobicy.com/jobs?search_keywords=Workflow%20automation.md)[CLI development](https://jobicy.com/jobs?search_keywords=CLI%20development.md)[MCP](https://jobicy.com/jobs?search_keywords=MCP.md)[Experiment design](https://jobicy.com/jobs?search_keywords=Experiment%20design.md)[Impact measurement](https://jobicy.com/jobs?search_keywords=Impact%20measurement.md)[Product analytics](https://jobicy.com/jobs?search_keywords=Product%20analytics.md)[Stakeholder communication](https://jobicy.com/jobs?search_keywords=Stakeholder%20communication.md)

Sample interview questionsTell us about a product, company, tool, or open-source project you shipped that people actively use.I would describe the customer problem, my personal ownership, the fastest viable version I shipped, adoption evidence, and the measurable outcome. I would also explain what I changed after observing real user behavior.

How would you identify the first problem worth solving with Mercury Command, CLI, or MCP?

I would begin with direct customer observation and workflow mapping, looking for repetitive, high-friction financial tasks with clear success metrics. I would validate the pain with users, prototype a narrow workflow quickly, and measure time saved, completion rate, error reduction, and repeat usage before expanding scope.

Describe an AI experiment that failed and how you decided whether to stop, revise, or continue it.

I would define an explicit hypothesis and success threshold before building, then evaluate the experiment against user value, reliability, and operational cost. If it missed the threshold, I would identify whether the failure was due to the problem, model behavior, data quality, or interaction design, then either run one focused follow-up experiment or stop it.

How would you design evaluations for an AI agent that can access verified financial data and stage actions for approval?

I would build a representative task set with expected outputs and safety constraints, covering correctness, authorization, grounding, tool selection, and failure handling. I would combine automated regression evaluations with human review of high-risk workflows, track production outcomes, and require approval gates for consequential actions.

How do you balance speed with craft when operating as a small autonomous team?

I prioritize craftsmanship where it affects user trust, reliability, security, and iteration speed, while avoiding polish that does not change the learning outcome. I ship a focused version, instrument it, gather customer feedback quickly, and invest further only when the evidence supports it.

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