Top Tech Jobs & Startup Jobs in Austin, TX

Leads technical direction and execution for major products and systems. Designs scalable, reliable architectures across backend, frontend, APIs, databases, and infrastructure; remains hands-on with coding, prototyping, reviews, debugging, and production support. Drives architecture trade-offs, converts ambiguous problems into technical plans, improves engineering quality, leads critical initiatives, and mentors engineers across distributed international teams.

Own product areas within AI Investing and related financial services, defining user problems, product direction, roadmaps, metrics, and launch plans. Collaborate with engineering, design, data, operations, compliance, and business teams to simplify complex financial products. Launch features, monitor performance, use data and feedback to drive iterations, and balance speed, user experience, business impact, compliance, and operational complexity.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; and use research, feedback, experiments, and performance data to iterate rapidly after launch.

Owns the end-to-end product strategy and execution for AI-powered systems. Defines requirements, evaluation frameworks, quality standards, and system behavior while balancing quality, latency, cost, reliability, safety, and user experience. Partners closely with ML, backend, and client engineering teams to identify failure modes, improve model performance, prioritize roadmaps, and deliver predictable, useful, trusted AI experiences.

Own the end-to-end product direction and quality of an AI email application. Define AI and system requirements, translate model capabilities into product decisions, balance quality, cost, latency, reliability, safety, and user experience, and partner with ML, backend, and client engineers. Establish evaluation frameworks, quality standards, feedback loops, and failure recovery strategies while driving prioritization, execution, experimentation, and user trust in a zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI and system requirements, evaluation frameworks, quality standards, and measurable outcomes. Partner closely with ML, backend, and client engineering teams to make trade-offs across quality, latency, cost, reliability, safety, and user experience. Drive prioritization, specifications, experimentation, failure-mode mitigation, and continuous iteration in a technical zero-to-one environment.

Own AI stockbroking and other financial product areas from user problem discovery through launch and iteration. Define product direction, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; use research, feedback, experiments, and performance data to improve products; and balance speed, user experience, business impact, compliance, and operational complexity.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI and system requirements, translate model capabilities into product decisions, establish evaluation frameworks, and balance quality, latency, cost, safety, reliability, and user experience. Partner closely with ML, backend, and client engineers to identify failure modes, improve system behavior, prioritize execution, and build predictable, trustworthy AI experiences.

Own AI Investing product areas across financial services. Define user problems, product direction, scope, metrics, roadmaps, and launches; collaborate with engineering, design, data, compliance, operations, and business teams; simplify complex financial journeys; use data and feedback to improve products; and drive post-launch iteration and performance.

Leads the end-to-end execution layer for machine learning, including data pipelines, large-model training and fine-tuning, evaluation, inference, deployment, monitoring, and reliability. Designs scalable GPU-based systems, optimizes performance and cost, partners with research and application engineering, and translates model capabilities into measurable production improvements. Also provides technical leadership, judgment, and support for high-impact ML delivery.