New opportunity

Product Manager, AI-Native Builder

Bond Sports · New York, United States

About this role

Bond Sports is hiring a NYC-based product manager who builds software, not just specifications. Working directly with the Chief Product Officer, the role owns features from customer discovery through design, implementation, production release and measurement. Bond Sports provides SaaS software for youth and recreational sports facilities, including workflows for registration, rentals and events, payments, scheduling, accounting and reporting.

Responsibilities

  • Talk with customers and internal stakeholders, identify problems, define requirements, design workflows and resolve edge cases.
  • Build prototypes, internal tools, AI agents and production features across APIs, data, payments, scheduling, permissions, reporting and consumer experiences.
  • Test, ship and measure features, then improve them based on use. Apply AI tools throughout research, product, design, development, testing and analysis.
  • Evaluate new models and agent frameworks; improve AI workflows through better context, fewer tokens, model selection and reusable approaches. Identify processes to automate or simplify and build AI-powered customer experiences.

Requirements

  • At least 3 years of experience building software products, with the ability to turn ambiguous problems into clear product requirements and deliver features end to end.
  • Comfort with APIs, databases and modern web applications; sound UX judgment; and regular use of AI tools such as Claude, Cursor or Codex.
  • Curiosity about emerging AI technology, practical judgment, initiative and strong communication with customers, engineers, designers and business teams. The role calls for someone willing to improve team tools and processes.

Preferred

B2B or B2B2C SaaS, startup, AI agent or AI-powered product experience; familiarity with scheduling, payments, commerce, memberships, marketplaces or multi-location businesses; or experience in sports, fitness, recreation or sports facility management.

Compensation and location: NYC-based, full-time role with a $100,000–$200,000 salary range, leveled by experience and demonstrated scope. Full-time employees receive equity; benefits include health, dental and vision coverage, other benefits through Justworks, and an available 401(k).

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Product managementCustomer discoveryRequirements gatheringPrototypingUX designSoftware developmentAPIsDatabasesWeb applicationsAI agentsAI-powered product developmentClaudeCursorCodexTestingProduct analyticsWorkflow automationModel selectionStakeholder communication

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Own features from discovery to measured use

    The role covers customer conversations, requirements, UX, building, testing, production release and measurement. Prepare to explain an end-to-end approach that uses customer feedback and usage data to decide what to improve.

  • Build across connected facility workflows

    Bond Sports names registration, rentals and events, payments, scheduling, accounting and reporting, and expects work across APIs, data and permissions. Prepare to reason through a workflow’s dependencies and edge cases, rather than treating a feature as an isolated screen.

  • Use AI tools to deliver useful software

    The role calls for AI-assisted prototyping and coding, internal tools, agents and customer-facing features. It also calls for testing models and frameworks and improving context, token use, model selection and reusable workflows. Prepare to distinguish a useful AI application from an interesting demonstration.

  • Ship quickly without leaving a mess

    The posting emphasizes frequent shipping, autonomous execution, testing and follow-through. Its stated success criteria include features customers use and measurable impact without creating a mess behind them. Prepare to describe how release checks and post-launch measurement fit into a fast build cycle.

Likely challenges to think through

  • Speed versus reliability in operational workflows

    Likely preparation hypothesis: moving quickly across payments, scheduling and permissions could make edge cases costly to miss; this is not a stated Bond Sports problem. Prepare a method for identifying consequential failure cases, testing them and choosing when a feature is ready to release.

  • Breadth versus depth of customer understanding

    Likely preparation hypothesis: workflows may differ between independent facilities and large multi-location organizations, while the role must make product decisions without waiting for every specialist. Prepare to show how customer discovery would separate a shared problem from a site-specific request.

  • AI experimentation versus maintainable delivery

    Likely preparation hypothesis: new models and agent frameworks can accelerate building but also create duplicated or hard-to-evaluate work; the posting does not say this is currently happening. Prepare criteria for testing an approach, selecting a model and deciding whether it merits a reusable workflow.

03

Practice your answers

Technical practice

Suggested practice: How would you turn a facility operator’s vague complaint into a feature you can build?

What to coverThe role calls for customer conversations, clear requirements and end-to-end ownership. Explain how you would identify the underlying problem, define a workable scope and decide what evidence would show the feature helped.

Suggested practice: How would you design a straightforward rental or event-scheduling workflow?

What to coverBond Sports lists rentals, events and scheduling among its product workflows. Walk through the user’s goal, the main steps and how you would uncover edge cases before building.

Suggested practice: How would you plan a feature that spans a web interface, an API and a database?

What to coverThe posting calls for comfort with APIs, databases and modern web applications. Describe how you would map the data flow, choose what to prototype first and check that the pieces work together.

Suggested practice: How would you handle a change that touches payments and reporting?

What to coverPayments, accounting and reporting are stated product areas. Discuss how you would define expected behavior, test consequential edge cases and verify that the resulting records and reports agree.

Suggested practice: How would you approach permissions in a multi-location workflow?

What to coverThe role involves permissions, and Bond Sports serves both independent facilities and multi-location organizations. Explain how you would identify who needs access to what, make the workflow understandable and test that access behaves as intended.

Suggested practice: When would an AI agent be useful in a customer-facing experience?

What to coverThe role includes building AI agents and identifying AI-powered customer experiences. Explain how you would choose a narrow problem, define what the agent may do and assess whether it improves on a simpler workflow.

Suggested practice: How would you compare models or agent frameworks for a product task?

What to coverThe page specifically mentions testing new models and frameworks, improving context, reducing tokens and selecting models. Describe a task-based comparison that weighs output quality, reliability, cost and ease of reuse rather than novelty alone.

Suggested practice: How would you use AI tools while building a production feature?

What to coverThe role expects regular use of AI across development and testing, including tools such as Claude, Cursor or Codex. Explain what you would delegate to a tool, what you would verify yourself and how you would avoid carrying untested output into production.

Suggested practice: How would you test, release and measure a feature you built?

What to coverThe posting asks this PM to build, test, ship, measure usage and improve the result. Describe a focused test plan, a release check and measures tied to the customer problem rather than shipment alone.

Suggested practice: How would you decide whether to automate, simplify or remove an internal process?

What to coverThe role calls for finding process improvements and reducing duplicated work. Explain how you would identify the underlying need, compare those three options and check whether the change saves effort without introducing new problems.

Behavioural practice

Suggested practice: Tell me about a time you clarified an ambiguous customer problem.

What to coverUse a real situation, your discovery and requirements-setting actions, and the result. The relevant role emphasis is identifying good problems through customer contact, not just executing assigned work.

Suggested practice: Tell me about a feature you owned from idea through release and follow-up.

What to coverDescribe the situation, what you personally did across requirements, UX, building and testing, and what happened after release. Be precise about your scope and any evidence of use or improvement.

Suggested practice: Tell me about a time you resolved competing stakeholder views on a product decision.

What to coverGive a real example involving the people and constraints, how you communicated and made the decision, and its result. This prepares for the role’s collaboration with customers, engineers, designers and business teams.

Suggested practice: Tell me about a time you introduced a better tool or working process.

What to coverExplain the initial friction, how you tested and encouraged the change, and what the outcome showed. The posting values practical adoption of useful AI tools and improved team workflows, rather than novelty for its own sake.

Suggested practice: Tell me about a time you balanced shipping quickly with keeping a feature maintainable.

What to coverSet out the situation, the trade-off you faced, the safeguards or scope decisions you made, and the result. This reflects the stated aim to ship quickly without creating a mess behind you.

02

Sharpen your knowledge

Topics to prepare

  • End-to-end product ownership

    Review how discovery, requirements, UX decisions, implementation, release and measurement connect. As a revision exercise, outline the decisions and evidence needed at each stage for a hypothetical facility-booking improvement. The posting explicitly expects ownership from customer problem through production and follow-through.

  • Sports-facility workflows and edge cases

    Map how registration, rentals or events, scheduling and payments might interact; list questions you would ask before deciding how to handle a conflict or cancellation. These are named product workflows. The specific scenarios are preparation suggestions, not stated Bond policies.

  • APIs, data and permissions

    Sketch how a modern web application might read and update a booking through an API and database, then identify permission and data-consistency questions. The role calls for comfort with APIs, databases and web applications and work across permissions and scheduling.

  • Practical AI model and workflow choices

    Compare two hypothetical AI-assisted tasks on output quality, context provided, token use and whether a reusable workflow would help. The posting explicitly emphasizes testing models and agent frameworks and improving context, model selection and duplication.

  • Product measurement and iteration

    For a hypothetical released feature, define a usage measure, a customer-outcome signal and one follow-up question if adoption is low. The role explicitly involves measuring shipped work, improving it based on use and making a measurable difference.

Skills to revise

  • Turn discovery into requirements

    Practice a short customer interview about a hypothetical scheduling problem, then write a problem statement, scoped requirements, open questions and edge cases. This rehearses the stated responsibility to talk with customers and turn messy problems into clear solutions.

  • Prototype a straightforward workflow

    Create a simple interactive or coded prototype for a hypothetical facility registration or rental flow; walk through the happy path and one failure state. This is a suggested exercise for the posted expectation to design workflows and build prototypes with sound UX judgment.

  • Build and test a small web feature

    Implement a small practice feature that reads and updates records through an API, handles a permission check and includes tests for an edge case. This rehearses the explicitly stated expectation to build and test features across APIs, data and permissions; it is not a claim about Bond's stack.

  • Use AI tools with verification

    Use Claude, Cursor, Codex or a similar tool to help develop the practice feature; record what context you supplied, what you verified yourself and whether a different approach reduced rework. The posting says AI tools are a normal part of this role and calls for practical judgment about their use.

  • Explain a release-and-iteration plan

    Present a brief plan for testing, shipping and observing the practice feature, then name one change you would consider based on usage evidence. This mirrors the posted responsibility to ship, measure and improve work rather than stop at a specification.

04

Your preparation checklist

  • Map the product workflows

    Review the stated areas—registration, rentals and events, payments, scheduling, accounting and reporting. Prepare a one-page map of how a facility operator might move between them; label assumptions to verify with Bond Sports.

  • Prepare an end-to-end feature walkthrough

    Choose a software feature you helped build, if available. Show the customer problem, requirements, UX decisions, implementation, testing, release and usage evidence; distinguish your work from collaborators’ contributions.

  • Practice resolving a messy workflow problem

    For a hypothetical facility scheduling or registration issue, draft discovery questions, a simple user flow, edge cases and a small first release. This is a preparation exercise, not a stated Bond Sports project.

  • Show how you would build and validate it

    Sketch a prototype or technical plan covering relevant APIs, data, permissions and tests. Be ready to explain trade-offs and what you would need to learn before shipping a production feature.

  • Document a practical AI workflow

    Prepare an example of how you use tools such as Claude, Cursor or Codex across building or testing. Explain how you check outputs and decide whether a model, agent or reusable workflow is useful rather than merely novel.

  • Define measurement and follow-through

    For your walkthrough or practice exercise, specify a usage signal, a customer outcome and a post-release improvement decision. The role explicitly includes measuring features and improving them based on use.

  • Bring questions for the Chief Product Officer

    Ask how customer problems are surfaced, how production ownership works across product and engineering, and where AI tools or customer-facing AI experiences could make a measurable difference.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: Nov 4, 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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