Top Tech Jobs & Startup Jobs in NYC, NY

Build developer awareness and adoption of Koah’s AI advertising SDKs through demos, apps, community initiatives, content, events, hackathons, and social media. Gather developer feedback, translate it into product roadmap improvements, support developer-focused campaigns, and help define developer relations strategy for AI ad monetization. The role requires technical credibility, community engagement, and close collaboration with engineering, product, go-to-market, and marketing teams.

Own Koah's content strategy and execution to drive awareness and pipeline: write founder and thought-leadership content, build SEO/GEO and ABM content, create data-backed claims, produce customer case studies, support product launches, and maintain a content calendar aligned to ICP and sales stages.

Conduct personalized, high-volume outbound through email, LinkedIn, and phone to identify and engage AI apps, developer platforms, and digital publishers. Research target accounts, identify decision-makers, write insight-driven outreach, book qualified meetings, provide handoffs to senior salespeople, and improve targeting and messaging through testing. The role is fully in-person in New York City and offers a path toward closing and senior go-to-market roles.

As a Software Engineer - Backend at Koah Labs, you will build scalable data models, optimize performance, and develop flexible tracking systems for ad engagement, using a diverse tech stack.

The Product Designer will create experiences for advertisers and publishers, focusing on campaign tools, SDK onboarding, and ad format systems while collaborating across various touchpoints.

Design, build, and maintain infrastructure for an adtech platform. Operate production systems at scale, optimize performance, own platform layers, run experiments, and ensure reliability across a broad set of systems.

Build and scale production-quality full-stack systems, lead cross-functional initiatives, optimize matching algorithms, create high-quality user experiences, and use experimentation and data to drive product decisions.

Partner with product engineering to design and analyze experiments, apply causal reasoning and statistical modeling to marketplace data, and drive data-informed product decisions across the engineering org.