Overview
You'll own the partnerships channel at Firecrawl - a web scraping/data extraction tool built for LLM engineers and AI developers. You're prospecting potential integration partners (think AI agent platforms, LLM orchestration tools, data pipeline services), running outbound to get meetings, working with their teams to scope technical integrations, and closing partnership agreements. You report directly to Eric (co-founder) and this is one of their main growth channels.
Role Snapshot
| Aspect | Details |
|---|---|
| Role Type | Partnerships BD - mix of sales, product, and technical coordination |
| Sales Motion | Outbound-heavy with some inbound interest from the open-source community |
| Deal Complexity | Consultative - each partnership has different technical and commercial terms |
| Sales Cycle | 1-3 months for smaller partnerships, 3-6 months for platform integrations |
| Deal Size | Varies widely - rev share deals, co-marketing agreements, or strategic integrations |
| Quota (est.) | Likely measured on # of partnerships shipped and pipeline influenced, not pure revenue |
Company Context
Stage: Seed/Series A (YC S22 - roughly 2 years old)
Size: 40 employees
Growth: Open-source project with developer community momentum; hiring for critical GTM roles
Market Position: Category participant in the growing LLM infrastructure space - competing with other scraping tools but differentiated by AI-ready output formats
GTM Reality
Pipeline Sources:
- 60% Outbound - you're identifying companies building AI agents, LLM apps, data orchestration tools and cold reaching out
- 30% Inbound - some companies discover Firecrawl through GitHub/docs and reach out about partnerships
- 10% Network/Warm intros - YC connections, developer community referrals
Who You Work With:
- You're the first (or one of the first) partnerships hires
- Work directly with Eric (co-founder) on deal strategy and closes
- Coordinate with engineering team to scope and ship integrations
- No dedicated partner engineering team yet - you're managing technical coordination
Competitive Landscape
Main Competitors: Other web scraping APIs (Apify, Browserless, Bright Data), traditional scraping libraries (BeautifulSoup, Scrapy), companies building their own scrapers
How They Differentiate: LLM-ready output formats (markdown, clean JSON), reliability at scale, built specifically for AI use cases
Common Objections: "We already have a scraping solution", "We can build this internally", pricing concerns for high-volume usage
Win Themes: Saves engineering time, handles JavaScript/dynamic content reliably, optimized for token efficiency in LLM applications
What You'll Actually Do
Time Breakdown
Outbound Prospecting (35%) | Active Partnership Deals (30%) | Integration Coordination (20%) | Internal Strategy (15%)
Key Activities
- Identify and research potential partners: You're mapping the LLM/AI tooling landscape - who's building agent frameworks, which companies have data pipeline products that need scraping, what platforms could benefit from Firecrawl integration. Lot of time on Product Hunt, GitHub, AI Twitter, YC directories.
- Run outbound sequences: Cold emails and LinkedIn messages to BD/partnerships people at target companies. You're explaining why an integration makes sense for both sides. Response rates are low (5-10%) because everyone's getting partnership pitches.
- Take partner discovery calls: When you get a meeting, you're qualifying fit - what's their user base, what's the technical integration scope, what's the commercial model (rev share, co-marketing, strategic). You need to understand both business and technical feasibility.
- Coordinate integration builds: Once a partnership is agreed, you're the PM - writing specs, working with Firecrawl engineers and the partner's team to ship the integration, managing timelines. Lots of Slack threads and GitHub issues.
- Negotiate commercial terms: Working with Eric on pricing, rev share splits, GTM commitments. Each deal is custom at this stage.
- Create co-marketing assets: Case studies, joint blog posts, documentation. You're often writing the first draft yourself.
The Honest Reality
What's Hard
- Most partnership conversations go nowhere - companies are interested but don't prioritize the integration, or the commercial terms don't make sense
- You're wearing multiple hats - part sales, part product manager, part project manager. No playbook exists yet.
- Integration timelines slip constantly because partner engineering teams are busy with their roadmap
- Revenue attribution from partnerships is fuzzy in the early days - hard to prove direct impact
- You're building the partnerships function from scratch, so lots of process creation and figuring out what works
- Technical coordination is messy when you're not an engineer yourself - you're translating between teams
What Success Looks Like
- Ship 3-5 meaningful integrations in your first 6 months that generate real usage
- Build a pipeline of 15-20 active partnership conversations
- At least 2-3 partnerships that drive measurable new customer acquisition or expansion
- Create repeatable processes for partner evaluation, onboarding, and integration management
Who You're Selling To
Primary Partners:
- BD/Partnerships leads at AI agent platforms (LangChain, AutoGPT-type tools)
- Product managers at LLM orchestration companies
- GTM leaders at data pipeline/ETL tools
- Developer relations at cloud/infrastructure platforms
What They Care About:
- Does this solve a real problem their users have?
- How much engineering effort is required on their side?
- What's the commercial upside (revenue, user growth, retention)?
- Will Firecrawl be a reliable partner (uptime, support, longevity)?
- Can they launch this partnership in a meaningful way (co-marketing, case studies)?
Requirements
- 2-4 years in partnerships, BD, or sales at a developer-focused or infrastructure company
- You understand technical products well enough to have credible conversations with engineers and scope integrations
- Track record of identifying, closing, and managing partnership deals
- Comfortable with ambiguity - you're building this function from the ground up
- Scrappy and hands-on - you'll write the first email templates, build the first partner decks, manage projects in Notion yourself
- Bonus: Experience in the AI/ML/data infrastructure space
- Bonus: Some coding background (can read APIs, understand webhooks, debug integration issues)