Top Tech Jobs & Startup Jobs

Build fullstack interfaces, APIs, inference workflows, internal tools, and open-source features for document understanding systems. Optimize model inference and framework reliability, improve developer experience, engage with GitHub and Discord communities, and support customer implementations. The role requires high ownership across backend, frontend, performance, product, and user-facing engineering.

Own the full enterprise sales cycle, including inbound qualification, discovery, demos, technical evaluations, pricing, security reviews, procurement, closing, onboarding, expansion, and renewals. Build the company’s outbound motion, define the ideal customer profile, maintain rigorous CRM and forecasting processes, create sales playbooks, and provide structured product feedback. The role requires selling technical AI infrastructure to engineering and executive buyers while helping establish the company’s broader sales function.

Own Datalab’s marketing as its first dedicated marketer, leading open-source model launches, content strategy, ghostwriting, SEO, paid acquisition, website conversion, community engagement, sales enablement, and AI-assistant discoverability. The role combines hands-on writing and technical marketing with funnel measurement, positioning, launch operations, and customer-focused content across developer and regulated industries. Success includes improving inbound pipeline, systematizing launches, and increasing Datalab’s visibility in AI-generated recommendations.

Build and maintain Datalab’s open-source repositories and Python SDK, including shipping features, releases, demos, benchmarks, tutorials, and technical content. Engage developers across GitHub, Discord, X, Reddit, and other communities; support product launches; gather developer feedback; and help shape the roadmap. The role requires strong production engineering, open-source contribution, public technical communication, and ownership from design through maintenance.

Train, evaluate, and optimize deep learning models for OCR, document understanding, layout, text recognition, and structured extraction. Build datasets and reproducible evaluation pipelines, benchmark architectures, optimize inference across GPU and CPU hardware, and integrate models into production APIs and products. Collaborate with users and partners to identify quality gaps, conduct experiments, and translate research findings into practical customer-facing capabilities.