Top Tech Jobs & Startup Jobs in Austin, TX

Own metrics and datasets that measure cloud, storage, streaming, and ML infrastructure efficiency. Build end-to-end analytics: metric frameworks, pipelines, dashboards, and narratives that inform executive and engineering investment decisions. Pioneer methods to quantify AI and agent-driven productivity, identify efficiency opportunities, and partner with data engineers and data scientists to deliver trusted, reusable insights and recommendations.

Conduct research and develop identity and probabilistic-matching models for Netflix's ad-supported tier. Apply ML and statistical methods, ensure privacy-preserving solutions (differential privacy, anonymization), prototype and deploy production models, deliver datasets and tools, and partner with product, engineering, and business stakeholders to measure and drive impact.

Lead and deliver high-ambiguity, multi-team AI product programs from concept to production. Align engineering, applied science, product, and external partners; plan milestones, manage dependencies, and create decision-ready communications. Build lightweight program rituals, evaluate technical/business tradeoffs, and guide ML lifecycle execution for generative AI, search, discovery, and new content modalities.

Design, build, and maintain production-scale data systems and web-scale datasets (Spark/Flink) to support personalization ML. Drive dataset/schema design, collaborate with algorithm teams, and improve distributed big-data processing for member experience.

Build and prototype supervised ML models to predict ad campaign delivery outcomes (reach, frequency, contention). Own feature engineering, evaluation frameworks, interpretability, and partner with ML engineers to deploy and monitor models at scale. Collaborate across product, engineering, and sales to drive adoption and measure lift versus simulation baselines.

Lead and optimize Enterprise XD design operations: translate strategy into deliverables, drive large-scale programs, manage prioritization, resourcing, roadmaps, cross-functional partnerships, and advocate for rigorous design practices.

Provide frontline technical support for developer-facing productivity tools: troubleshoot incidents, automate support tooling, create runbooks and documentation, drive product and operational improvements, and partner with engineering and PM teams to improve developer experience and reduce friction.

Own and improve the end-to-end developer experience for AIMS ML teams: local/remote dev environments, build/test infrastructure, CI/CD, and internal platforms. Design, build, and operate large-scale build and CI systems, remove developer friction, instrument workflows, drive adoption, and set technical standards. Partner with ML researchers to prioritize productivity investments and evaluate/integrate GenAI-powered developer tooling with safety guardrails.

Lead end-to-end product analytics for New Content Experiences, partnering with Product, Design, Data Science, and Engineering to analyze large datasets, build tools, ensure data quality, and inform product strategy and decision frameworks.