Top Tech Jobs & Startup Jobs in Austin, TX

Lead state government sales for FutureFit AI: own full sales cycle from discovery to signed contracts, build multi-stakeholder relationships, navigate procurement and RFPs, monitor policy and funding cycles, and help create a repeatable go-to-market playbook while aligning internal teams.

The Brand and Content Lead will manage the brand presence and video content for FutureFit AI, focusing on consistency and impactful storytelling across platforms, particularly LinkedIn. Responsibilities include overseeing video production, written content creation, and collaboration with marketing teams.

Own and elevate the platform visual language and design system, lead end-to-end design for complex, multi‑persona features, embed accessibility (WCAG 2.2 AA / Section 508), run user research, design AI‑mediated experiences, and mentor teammates to maintain design quality and coherence across job seeker, staff, employer, and agency admin surfaces.

Own the Customer Workflows domain end-to-end: employer/recruiter portals, business service and agentic workflows, and agentic AI use cases. Drive AI-native prototyping, discovery, roadmap, technical scoping, evaluation frameworks, and cross-functional delivery with Engineering, Data, Sales, and CS to produce scalable, data-informed product outcomes.

Build and operate ingestion and transformation pipelines, model and test core warehouse data, enable analytics and reporting, and maintain ML data infrastructure feeding matching and recommendation models. Partner with Engineering, Product, and Data Science to ensure reliable, documented, and trusted data at scale.

Build and ship full‑stack web features end-to-end using React/TypeScript and Node.js. Partner with Product, Design, and Data to deliver reliable, well-tested software, set and uphold code quality, make technical decisions, and mentor others (at senior level). Own features from design through production and improve tooling, testing, and observability to increase team velocity.

Lead assessment, redesign, and hands-on implementation of data pipelines, ML workflows, and platform infrastructure. Produce prioritized system designs, harden production pipelines, improve observability, reliability, and data quality, and mentor the data team.