3 months ago
Base Salary
$150k - $300k/yr
Responsibilities
- Own and evolve Modal’s marketing site, docs site, and technical web experiences as high-quality engineering systems.
- Build bespoke interactive projects that turn deep technical work into useful web experiences for developers and agents.
- Instrument public-facing content, including human-facing marketing pages and agent-targeted documentation.
- Improve internal publishing flows so teams can ship quality content quickly while maintaining site quality.
- Partner with Product Engineering, Design, Data, and Growth to ship polished, measurable pages, documentation experiences, campaigns, and experiments.
Requirements
- Evidence of building and shipping modern web applications end-to-end; demonstrated work matters more than years of experience.
- Experience with agentic development flows in which agents consume marketing content and documentation to help developers build on Modal.
- Ability to build polished, responsive web experiences with limited specification.
- Business-owner mindset with strong data intuition around conversion, activation, developer trust, launch speed, measurement, and tradeoffs.
- Interest in developer-facing AI infrastructure and credible web experiences for engineers.
- Ability to work in person in Modal’s NYC office.
- Preferred familiarity with SEO, AEO, structured content, agentic discoverability, marketing or documentation sites, product-led-growth surfaces, growth experiments, A/B tests, funnel instrumentation, or polished web and documentation projects.
- Preferred familiarity with Svelte/SvelteKit, TypeScript, Tailwind, Vite, MDX/SVX, Sanity, Playwright, Vitest, Segment, Snowflake, PostHog, Sentry, Algolia/DocSearch, Fillout, Pylon, and Ashby.
Benefits
- In-person work in the NYC office is required.
- Opportunity to join an early-stage, fast-growing AI infrastructure company with opportunities for growth.
Tech Stack
Categories
FrontendGrowth Engineering
About Modal
Customers rely on Modal for instant GPU access, sub-second container starts, and native storage, so it's simple to serve low-latency inference, fine-tune models, and access production-ready sandboxes at scale. Every era of computing came with new workloads that previous infrastructure couldn't serve: mainframes, databases, the cloud. Each time, the company that rebuilt the layer underneath defined the decade. AI is no different, except it touches everything instead of one slice. The window to build is open right now.