Top AI & Machine Learning Jobs in Austin, TX

Lead productionization and optimization of ML models for a global, serverless inference platform. Build benchmarking/evaluation tooling, improve inference performance (quantization, compilation, batching), integrate models with heterogeneous GPUs and accelerators, enhance deployment workflows and observability, and mentor engineers while driving ML production best practices.

Build and operate AI-driven People Ops systems on the Cloudflare developer platform: ship production workflows for hiring, onboarding, recruiting automation, self-service, AI copilots, data reconciliation, observable audit pipelines, and custom interview tooling. Integrate systems securely, implement authorization/audit logging, and work cross-functionally with HR, Legal, IT, and Security.

Design and build AI Gateway systems for high-volume, latency-sensitive inference at the edge. Implement APIs, routing, observability, controls, developer workflows and tooling. Prototype ambiguous ideas to production, improve reliability, performance, cost-efficiency and debuggability, and mentor engineers while dogfooding AI-native tools, agents, and automation to accelerate development and operations.

Build scalable, reliable ML and GenAI models and backends; partner with product, engineering, and business stakeholders; improve data collection and quality with data engineers; train and deploy models at scale; publish model insights; analyze trends with statistical significance; present findings to varied audiences; and hire, mentor, and grow the Austin data science team.

Build and maintain Cloudflare's new vector database offering: design, implement, and operate distributed, low-latency cloud systems. Own features across the stack, ensure reliability and performance, write design documents, and apply machine learning and data retrieval expertise using TypeScript, Go, Rust, and related technologies.

Lead design and build of a scalable, multi-tenant AI/ML platform powering traditional ML, LLMs, and agentic systems. Own end-to-end architecture, production-grade backends, orchestration, LLMOps, vector DB integration, and cross-functional delivery while enforcing engineering standards, security, and observability.

Design and build a global, serverless AI inference platform running on heterogeneous GPUs and accelerators. Optimize model scheduling, request routing, caching, and load balancing for low latency. Improve reliability and observability, lead cross-functional projects, and mentor junior engineers.