AI-native Partnerships Product Manager

Apply Job Type: Full-time

Description: Who are we? Truckloads is the leading mobile load board app in America, used by 300,000+ owner-operators to find freight, check rates, and run their businesses. We're building AI that fundamentally changes how carriers interact with loads - from AI-powered load verification and carrier onboarding to automated load booking. Our goal: make Truckloads the AI-native business assistant that owner-operators can't run their business without.

Role Purpose: Own and deliver Trucker Path's partner-led growth and marketplace roadmap end-to-end, with a strong bias toward technical execution. This includes fuel as one core program, plus parking and other marketplace offerings that require integrations, shared roadmaps, and reliable delivery.

This is an AI-native role: the PM is expected to use AI daily to speed up discovery, synthesis, writing, analysis, and technical validation, while maintaining high quality and clear accountability.

Key Responsibilities:

Own marketplace program growth (partner-led):

• Define strategy, funnel metrics, and quarterly roadmap per program.
• Partner with Growth, Sales, and Ops to ship experiments and measure lift.
• Drive partner-facing alignment on offers, eligibility, tracking, attribution, and reporting.

Own partnerships roadmap across marketplace surfaces:

• Run discovery with partners and internal stakeholders to define needs and success metrics.
• Translate requirements into a prioritized roadmap with clear tradeoffs.
• Coordinate delivery across Product, Engineering, Ops, and partner teams.

Drive integration and technical execution (hands-on):

• Own API and integration readiness: auth, permissions, data flow, edge cases, SLAs.
• Coordinate API specs, monitoring, incident response paths, and post-launch iteration.
• Build lightweight technical prototypes or validation tools to reduce ambiguity.

Operate AI-native product workflows:

• Use AI to synthesize user and partner conversations into crisp problem statements.
• Draft PRDs, API docs, test plans, rollout plans, and stakeholder updates faster.
• Use AI to accelerate analysis (SQL, dashboards, cohort views) and identify levers.
• Create reusable playbooks and templates (launch checklists, partner FAQ, enablement).

Measure outcomes and iterate:

• Define and track success metrics (adoption, conversion, retention, revenue, ops load).
• Run weekly review loops and propose iteration plans based on data and feedback.

Requirements:

• 3+ years in product management, ideally across platform, integrations, B2B, payments, or growth surfaces.
• Strong product judgment: can break down ambiguous problems and ship pragmatic solutions.

Technical execution capability required:

• Can work directly with APIs (REST/JSON), auth (API keys/OAuth), webhooks, and basic networking concepts.
• Can write and debug code for prototypes and internal tools (Python, JavaScript/TypeScript, or similar).
• Can query data (SQL) and produce lightweight analyses to validate hypotheses and monitor launches.
• Strong communication skills in English; can align engineers, non-technical stakeholders, and external partners.
• Strong program management across multiple stakeholders, dependencies, and timelines.
• AI-native working style: uses AI daily for research, synthesis, writing, analysis, and automation, while validating outputs with data and technical evidence.
• Familiarity with the U.S. trucking industry, or ability to ramp quickly through user and partner conversations.

Preferred requirements:

• Experience with fuel ecosystems, payments, loyalty, marketplaces, or B2B platform partnerships.
• Experience shipping third-party integrations and owning post-launch monitoring, incident patterns, and iteration.

What success looks like (first 90 days):

• Marketplace growth roadmaps (fuel, parking, and at least one additional program) are defined, instrumented, and shipping measurable improvements.
• A clear partnerships roadmap that sequences integrations and launches with aligned stakeholders, timelines, and success metrics.
• Reduced ambiguity for integrations: clean specs, faster validation cycles, fewer launch surprises.
• A repeatable AI-native operating cadence (docs, updates, analysis, launch playbooks) that increases execution speed without sacrificing quality.