Top AI Jobs in Austin, TX

Design, build, and deploy production-grade ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM applications, and scalable data pipelines with observability and MLOps practices for cloud and on-prem environments.

Design, build, and deploy production ML systems for ICS/xOT cybersecurity, including threat detection, anomaly detection, NLP/LLM features, data pipelines, observability, MLOps, and containerized deployments across cloud and on-prem environments.

As a Lead Software Engineer, you'll design and develop GenAI platforms, integrate LLMs, mentor engineers, and solve technical challenges for scalable solutions.

Lead Bankrate's organic demand system across AI search (AEO/GEO), SEO, and content. Define AI citation strategy, own organic revenue and measurement, build an AI-native team and agentic workflows, and partner with paid, brand, product, and analytics to drive durable, revenue-generating organic growth.

Lead GTM systems and applied AI for Revenue Operations: own non-Salesforce tech stack, drive GTM AI roadmap and COE, prototype and deploy AI workflows, govern data quality, optimize tooling, and manage a team of engineers and analysts.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Integrate and fine-tune existing AI models into production features, build evaluation frameworks and observability for AI performance, collaborate with data science and engineering, develop AI-powered features in Python, and troubleshoot and monitor models in real-world scenarios.

Lead design and delivery of enterprise integrations, automation, and AI-driven workflows across SaaS platforms. Architect scalable APIs, middleware, and conversational self-service experiences; partner with Security, Infrastructure, and Product; mentor engineers and drive long-term platform strategy, governance, and operational excellence.

Lead design, build, and deployment of scalable ML/AI models (including image/vision) to production. Collaborate with engineering and business teams, monitor model quality and bias, and apply data-driven solutions to predict and aggregate risk.

The Principal Embedded Software Architect will design the software architecture for low-power Edge AI MCUs, leading decisions for functionality and performance, partnering with hardware teams, and mentoring engineers.

Lead complex applied-AI technology programs from initiation through delivery and support. Define scope, plans, governance, risks, and dependencies; coordinate engineering, QA, product, and operations; use enterprise AI capabilities responsibly to accelerate planning and reporting; ensure production readiness and continuous improvement.

Design, prototype, and productionize generative AI features and agentic workflows. Build Python services, TypeScript/React UIs, data pipelines, evaluation and instrumentation, and safeguards. Partner with product and stakeholders to measure impact, ensure reliability, and mentor engineers.

Lead applied research on agentic AI for cybersecurity: set scientific direction, run experiments on efficient post-trained models, design evaluation/verification methodologies, partner with product and security teams, and translate research into secure, production-ready agentic systems.

Lead AI strategy, partner engagements, and operational execution across CrowdStrike products. Align R&D and product delivery to company AI strategy, prioritize initiatives, set organizational goals, drive thought leadership, and create order in complex cybersecurity AI environments while delivering customer value.

Lead research and engineering of production-grade LLM/GenAI systems for cybersecurity: design models and architectures, drive data labeling and evaluation, collaborate with product and engineering to deploy scalable solutions, mentor data scientists, set technical strategy, and advance AI safety, red-teaming, and applied ML innovations for detection and operational use.

Design and research agentic AI systems for cybersecurity: fine-tune and post-train LLMs (RLHF/RLAIF), build agent architectures and eval pipelines, optimize prompts and inference, collaborate with engineering to productionize prototypes, and establish rigorous benchmarks and metrics.

Lead applied research on agentic AI for cybersecurity: develop efficient post-trained models, agent designs, and rigorous evaluation/verification. Partner with security teams to translate research into products, run experiments, and publish novel techniques.

Lead development and deployment of large-scale LLM and GenAI solutions for cybersecurity. Drive research strategy, model training, evaluation, safety/red-teaming, and cross-functional productization. Mentor data scientists, set technical standards, and represent the team through publications and thought leadership.

Serve as field-facing technical authority on enterprise AI security: define AI technical strategy, design and deliver tailored demos/POVs, advise on LLM/RAG/agent risks and data protection, feed product roadmap, and create reusable enablement assets and training for sales and engineering teams.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead design and build of an AI-native marketing platform: integrations, multi-agent orchestration, UIs, tooling, analytics, CI/CD, auth/multi-tenancy, and production AI agents to automate marketing workflows and drive adoption across teams.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Lead Software Engineer responsible for designing, building, and maintaining scalable data pipelines and platform components (cataloging, quality, lineage, governance). Implements data modeling, partners with stakeholders to deliver production-grade code, automates operations, evaluates vendors, and leads Agentic Autonomous Lakehouse and engineering communities to drive platform innovation and operational stability.

The Applied AI Engineer will build and maintain AI agents, manage integrations and monitor performance, ensuring quality and continuous improvement of AI tools at Zello.