AI Jobs in Austin, Texas
The fastest-growing AI hub in America. Austin combines Silicon Valley innovation with Texas affordability. Tesla AI, Oracle, and hundreds of startups are hiring.
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Latest Austin AI Positions
Showing 7 of 7 opportunities
Freelance Annotator (English) - AI Trainer
Solutions Architect - Public Sector (SLED)
Software Engineer, Agentic AI
AI Security Engineer Technical Leader Hybrid/Remote
Senior AI Platform Engineer (Datagrid)
Staff AI Platform Engineer (Datagrid)
AI & Technology Manager - Office of the CTO
Why Austin for AI?
Rapid Growth
- Tesla AI and Autopilot headquarters
- Oracle, Google, Meta expanding
- Booming AI startup ecosystem
- University of Texas AI research
- Major tech company relocations
Texas Advantages
- No state income tax
- Significantly lower cost of living
- Strong job market and growth
- Vibrant tech and startup culture
- Great quality of life
Austin AI Compensation
Austin AI salaries are competitive, and with no state income tax, take-home pay often exceeds coastal cities. ML engineers earn $150k-$280k+ base.
Upcoming AI Events in Austin
Curated list of AI meetups, hackathons, and conferences coming up in the metro. Auto-refreshed from organizer calendars.
- May202026
AI builders night - Austin
Wed, May 20, 2026 · 11:00 PM UTCAustin AI Developers GroupAustin AI Developers Group **Important:** Register on the [event website](https://bit.ly/4wsVUUv) is required for admission. RSVP on Meetup is turned off. We are bringing together AI engineers, platform teams, and product leaders in Austin for an evening focused on how real-world AI systems actually get built and improved. You’ll hear how leading teams are evaluating models, closing the loop between experimentation and deployment, and building systems that hold up outside of demos. If you’re building AI systems and want to learn how others are approaching quality, evaluation, and scale, we'd love to see you there. **Agenda:** * 6:00 pm - doors open (networking in pre-function area) * 6:30 pm - How to build evals for coding agents, Amanda Gilbert, Solutions Engineering @ Braintrust * 6:50 pm - Demos and Q&A * 7:30 - 9:00 - Networking with F&B
- May212026
May 21 - Women in AI Meetup
Thu, May 21, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup Hear talks from experts on the latest topics in AI, ML, and computer vision on May 21. **Date, Time and Location** May 21, 2026 9 - 11 AM pacific Online. **[Register for the Zoom!](https://voxel51.com/events/women-in-ai-meetup-may-21-2026)** **Beyond Models: LLM-Guided Reinforcement Learning for Real-World Wireless Systems** Reinforcement learning agents often perform well in simulation but break down when deployed in real, non-stationary, constraint-driven environments such as wireless systems. This work explores using large language models not as annotators or reward hacks, but as a reasoning layer that guides RL decision-making with domain logic, scenario interpretation, and adaptive constraints. We present an architecture where the LLM provides structured, high-level advisory signals while the RL policy remains the final action authority to avoid hallucination-driven failures. Early experiments show that this hybrid setup improves robustness under distribution shifts and complex constraint scenarios where standard RL collapses. The goal is not to replace RL with LLMs, but to combine learning and reasoning into a more deployable control-intelligence framework. *About the Speaker* [Fatemeh Lotfi ](https://www.linkedin.com/in/fatemeh-lotfi-1a860359/?skipRedirect=true)is a Ph.D. researcher focused on integrating large language models and reinforcement learning for adaptive wireless control systems. Her work targets the limitations of classical RL under real-world uncertainty by introducing reasoning-driven guidance mechanisms using LLMs. She has contributed to multiple AI-for-infrastructure projects, including advanced O-RAN automation. **Responsible and Ethical AI in Healthcare: Building Trustworthy and Inclusive Intelligent Systems** In this session, I will discuss how Responsible AI principles: including fairness, transparency, accountability, and reliability can be practically embedded into healthca
- May272026
May 27 - Perceptron AI and FiftyOne for Video Understanding Workshop
Wed, May 27, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup Join us for a hands-on virtual session on May 27 exploring video-native multimodal AI and how to integrate cutting-edge video understanding models into your computer vision workflows. **Date, Time and Location** May 27, 2026 9:00 AM - 11:00 AM PST **[Online. Register for Zoom!](https://voxel51.com/events/getting-started-perceptron-ai-fiftyone-video-understanding-may-27-2026)** **Video-Native Multimodal Models for Video and Image Understanding** In this 20-minute talk, Akshat will introduce Perceptron’s latest release, a video-native multimodal model that matches or exceeds frontier models from Google and Alibaba on video and image understanding at a fraction of their inference cost. He’ll walk through the capabilities that move the needle for real video workloads: temporal grounding to clip precise events from long streams, egocentric reasoning for first-person and wearable contexts, and structured “thinking traces” that reason over motion and physical space. He’ll also cover the image-side advances production perception teams care about: reliable pointing, point-by-example one-shot visual search, dense counting, dial/gauge/clock reading, and structured document extraction. *About the Speaker* [Akshat Shrivastava](https://www.linkedin.com/in/akshatsh/) is the CTO and co-founder of Perceptron, previously leading AR On-Device at Meta and conducting research at UW. **Getting Started with Perceptron AI in FiftyOne** In the second half of the session, Harpreet Sahota will walk through how to get started using Perceptron’s video-native multimodal model within FiftyOne for real-world video understanding workflows. He’ll demonstrate how to connect to the API, explore multimodal outputs inside FiftyOne, and build practical workflows for tasks like temporal event analysis, visual search, and video dataset inspection. Attendees will leave with a hands-on understanding of how to integrate state-of-the-art video perc
- May272026
AI Meetup - Scale AI Workflows
Wed, May 27, 2026 · 10:30 PM UTCAustin AI Developers GroupAustin AI Developers Group **Important:** Register on the [event website](https://www.aicamp.ai/event/eventdetails/W2026052715) is required for admission. RSVP on Meetup is turned off. We are excited to partner with Coder for a special edition of our AI Meetup in May, focusing on Scale AI workflows in productions. AI is everywhere in development right now. Coding assistants. Autonomous agents. Background workflows. AI-generated PRs. But most teams are stuck in the same place: * Experiments work, production doesn’t. * AI writes code faster, but workflows break. * Agents generate output, but teams can’t trust or scale it. * And governance gets added too late, slowing everything down. This meetup is for developers and platform teams who want to understand what it actually takes to move from AI experimentation to production-ready workflows. We’ll focus on how teams are redesigning their development workflows to work with AI, not just adding tools on top. That includes how to structure workflows, manage agents, and introduce the right level of control without killing developer velocity. No hype. No tool comparisons. Just a clear look at why most AI workflows fail and what actually works at scale. **You’ll walk away with:** * \- A clear understanding of why most AI workflows break when moving to production * \- Insight into how AI changes the software development lifecycle\, not just coding * \- A practical model for structuring AI workflows across planning\, building\, and operations * \- Examples of how teams are introducing control and consistency without slowing developers down * \- A mental model for scaling AI usage across teams\, not just individual developers **Who this is for:** \- Beginner → intermediate software developers \- Platform engineers and DevOps folks supporting dev teams \- Anyone using \(or wanting to use\) AI tools beyond simple autocomplete **Agenda:** \* 5:30pm\~6:00pm: Checkin, Food and Networking \* 6:00pm\~6:15pm: Welcome/community upd
- Jun32026
Efficient Reasoning + Geometry and Dynamics of Hallucinations | Two 30-Min Talks
Wed, Jun 3, 2026 · 12:00 AM UTCAustin Deep LearningAustin Deep Learning **One\-Pass to Reason \+ The \(Hyper\-spherical\) Dynamics of Hallucinations \| Two 30\-Min Talks** **Details** **This will be a journal club event** Two Talks: 1. One-Pass to Reason: Token Duplication and Block-Sparse Mask for Efficient Fine-Tuning on Multi-Turn Reasoning (NeurIPS 2025 Workshop) [Link to Paper](https://arxiv.org/abs/2504.18246) 2. The *(Hyper-spherical)* Geometry and Dynamics of Hallucinations. *Based on Papers:* “A Geometric Taxonomy of Hallucination in LLMs”, Marin 2026 [https://arxiv.org/pdf/2602.13224v3](https://arxiv.org/pdf/2602.13224v3) “How Transformers Reject Wrong Answers: Rotational Dynamics of Factual Constraint Processing”, Marin 2026 [https://arxiv.org/abs/2603.13259](https://arxiv.org/abs/2603.13259) “Text Corpora as Concept Fields: Black-Box Hallucination and Novelty Measurement”, Kersting et al. 2026 [https://arxiv.org/pdf/2605.05103](https://arxiv.org/pdf/2605.05103)" **Speakers** 1. [Ritesh Goru](https://www.linkedin.com/in/ritesh-goru-3aa536133/), Member of Technical Staff at DevRev; [Prateek Jain](https://www.linkedin.com/in/prateek-jain/), Machine Learning Engineer at DevRev 2. [Connor Favreau, PhD,](https://www.linkedin.com/in/connorfavreau/) Principal Data Scientist at Central Health **Abstract** 1\. Fine\-tuning Large Language Models \(LLMs\) on multi\-turn reasoning datasets requires N \(number of turns\) separate forward passes per conversation due to reasoning token visibility constraints\, as reasoning tokens for a turn are discarded in subsequent turns\. We propose duplicating response tokens along with a custom attention mask to enable single\-pass processing of entire conversations\. We prove our method produces identical losses to the N\-pass approach while reducing time complexity from to and maintaining the same memory complexity for a transformer based model\. Our approach achieves significant training speedup while preserving accuracy\. 2\. RAG and LLM\-as\-Judge systems are among the
- Jun32026
AI Developer Workshop - Deploying AI Agents with Pulumi and AWS
Wed, Jun 3, 2026 · 10:30 PM UTCAustin AI Developers GroupAustin AI Developers Group **Important:** Register on the [event website](https://www.aicamp.ai/event/eventdetails/W2026060315) is required for admission. Most AI agent demos look great in a notebook, but getting them into production means dealing with authentication, secrets, IAM policies, container builds, observability, and repeatable deployments. The proof of concept rarely covers any of that. Join us for an evening of practical content on getting AI agents from prototype to production. Expect a hands-on workshop where you'll deploy a real multi-agent system on Amazon Bedrock AgentCore, with all infrastructure defined as code using Pulumi. **Agenda:** * \- 5:30pm\~6:00pm: Checkin\, food and networking * \- 6:00pm\~6:15pm: Welcome \+ intro to Pulumi * \- 6:15pm\~7:30pm: Hands\-on workshop: Deploying AI Agents * \- 7:30pm\~8:30pm: Open discussion\, Mixer and Closing\. **What You'll Learn:** * Go from a minimal agent to a multi-tool, multi-agent system on Amazon Bedrock AgentCore * Define your AI infrastructure as code * Patterns for authentication, secrets, IAM, and observability when running agents in production * Structure agent workflows that coordinate tasks, call external tools, execute code, and maintain state over time **Who Should Attend:** Engineers, platform teams, DevOps practitioners, and AI/ML developers who want to move past notebook demos and deploy real AI agents to production. Useful for anyone building on AWS, evaluating Bedrock AgentCore, or bringing infrastructure-as-code discipline to an AI workload. **Hands-on Workshop: Deploying AI Agents on AWS with Pulumi and Amazon Bedrock AgentCore** **Instructor:** Mitch Gerdisch — Solutions Architect, Pulumi **Abstract:** In this hands-on session, you'll go from a minimal agent to a multi-tool, multi-agent system running on Amazon Bedrock AgentCore, with all infrastructure defined as code using Pulumi. You'll build and deploy agents that coordinate tasks, call external tools, execute code, and
- Jun92026
June 9 - Visual AI in Healthcare: Ground Truth in the Foundation-Model Era
Tue, Jun 9, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup Learn how to handle expert label disagreement and build high performing fine-tuned medical foundation models for clinical imaging tasks. **Date, Time and Location** Jun 09, 2026 9:00 AM – 10:30 AM PST **[Online. Register for the Zoom!](https://voxel51.com/events/visual-ai-in-healthcare-ground-truth-in-the-foundation-model-era-june-9-2026)** Medical imaging teams are increasingly fine-tuning foundation models like UNI, MedSAM2, and BiomedCLIP on small in-house datasets. At that scale, label disagreement is a dominant cause of model failures, and the disputed ground truth is what regulators will ask you to defend. We'll build a medical imaging dataset in [FiftyOne](https://docs.voxel51.com/), surfacing and analyzing the cases where reviewers disagree. From there, we'll fine-tune a foundation model on cleaned data and use FiftyOne to evaluate where our model succeeds and fails, and which data is needed to move the model’s performance forward. **You’ll learn how to:** * Build a medical imaging dataset that preserves multiple expert annotations as first-class fields * Use FiftyOne views, embedding similarity, and confidence-disagreement signals to find the samples where reviewers split. * Run label-quality screens, near-duplicate detection, and active-learning sample selection using foundation model embeddings * Fine-tune a medical foundation model on a defensible dataset, with auditable and versioned experiment tracking * Filter and slice evaluation for regulatory and clinical readiness * Drive the pipeline with natural-language agents using the FiftyOne MCP Server and Skills to run the same curation, evaluation, and review workflows from your favorite AI tool **Who This Is For** * ML and computer-vision engineers in the medical imaging space * Data and annotation operations teams * Clinical AI and digital pathology leads * Regulatory and quality leads
- Jun172026
June 17 - How to Build Vision Data Agents with Tools, Skills, and MCP Workshop
Wed, Jun 17, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup In this session, you’ll learn how to build production-ready AI agents that can reason over your data, automate complex tasks, and integrate seamlessly into your existing stack using tools, skills, and the Model Context Protocol (MCP). **Time, Date and Location** Jun 17, 2026 9 AM Pacific Online. **[Register for the Zoom!](https://voxel51.com/events/how-to-build-vision-data-agents-with-tools-skills-and-mcp-june-17-2026)** AI agents are rapidly changing how teams build and scale machine learning workflows—but most implementations still rely on fragmented tools, manual processes, and brittle integrations. We’ll walk through how modern agentic systems move beyond simple prompts—leveraging structured tools like dataset operations, embeddings, evaluation pipelines, and model execution to take real action. You’ll see how these agents can tag data, run inference, evaluate performance, and surface insights automatically, all within a unified workflow. By combining natural language interfaces with programmable building blocks, teams can dramatically reduce manual effort, accelerate experimentation, and unlock faster decision-making across the ML lifecycle. Whether you're building data-centric AI systems, managing large-scale vision datasets, or exploring agentic workflows for the first time, this session will give you a practical blueprint for getting started. What you’ll learn: * **How AI agents actually work in production:** Move beyond hype—understand how agents use tools, memory, and structured workflows to execute real tasks * **Using tools to take action on your data:** Program agents to run operations like filtering labels, computing embeddings, evaluating detections, and more * **What “skills” are and how they enable multi-step workflows:** Learn how to package complex logic into reusable capabilities your agents can call on demand * **How MCP connects your models, tools, and agents:** See how MCP standard
- Jun242026
June 24 - Building Composable Vision Workflows in FiftyOne
Wed, Jun 24, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup This workshop explores the [FiftyOne plugin framework](https://docs.voxel51.com/plugins/index.html) to build custom computer vision applications. You’ll learn to extend the FiftyOne App with Python based panels and server side operators, as well as integrate external tools for labeling, vector search, and model inference into your dataset views. **Time, Date and Location** Jun 24, 2026 9 AM - 10 AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/building-composable-vision-workflows-in-fiftyone-june-24-2026)** **What you'll learn:** * **Build Python plugins.** Define plugin manifests and directory structures to register custom functionality within the FiftyOne ecosystem. * **Develop server side operators.** Write functions to execute model inference, data cleaning, or metadata updates from the App interface. * **Build interactive panels.** Create custom UI dashboards using to visualize model metrics or specialized dataset distributions. * **Manage operator execution contexts.** Pass data between the App front end and your backend to build dynamic user workflows. * **Implement delegated execution**. Configure background workers to handle long running data processing tasks without blocking the user interface. * **Build labeling integrations**. Streamline the flow of data between FiftyOne and annotation platforms through custom triggers and ingestion scripts. * **Extend vector database support.** Program custom connectors for external vector stores to enable semantic search across large sample datasets. * **Package and share plugins**. Distribute your extensions internally and externally
- Jun252026
June 25 - AI, ML and Computer Vision Meetup
Thu, Jun 25, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup Join our virtual meetup to hear talks from experts on cutting-edge topics across AI, ML, and computer vision. **Date, Time and Location** Jun 25, 2026 9AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/ai-ml-and-computer-vision-meetup-june-25-2026)** Large-Scale Scene Reconstruction via Local View Transformers Transformer-based models have advanced 3D scene reconstruction, but their quadratic attention limits scalability to large scenes. We introduce the Local View Transformer (LVT), which replaces global attention with locality-aware attention over neighboring views, conditioned on relative camera geometry. LVT decodes directly into 3D Gaussian splats with view-dependent color and opacity for high-fidelity rendering. Our approach enables scalable, single-pass reconstruction of large, high-resolution scenes. *About the Speaker* [Tooba Imtiaz](https://www.linkedin.com/in/tooba-imtiaz/) is a PhD candidate in Electrical and Computer Engineering at Northeastern University, working in the Machine Learning Lab. Her research focuses on 3D computer vision, novel view synthesis, and robust machine learning. She has published in top venues including SIGGRAPH Asia, CVPR, and ICLR, and has industry experience at Google. **Lessons learned from running AI workloads in production** He’ll share his “tales from the engine room” - practical insights from operating AI systems at scale, including the challenges of abstraction layers, the realities of data movement and hardware constraints, and how systems thinking is essential for building high-performance, secure, and responsible AI infrastructure. *About the Speaker* [Dave Hughes](https://www.linkedin.com/in/-davehughes-/) is CTO at Stelia. He was formerly CTO at Genesis Cloud, which pioneered what is now commonly known as 'neoclouds', and Principal Engineer/Interim Director of Engineering at Adjust GmbH where he built large-scale data warehousing and
Austin AI Community & Resources
Austin's AI scene is thriving — Capital Factory, UT Austin, and the Austin AI Alliance anchor a community of builders, researchers, and enterprise practitioners.
Community Groups & Meetups
- AI Tinkerers Austin
Monthly meetup for AI engineers and builders with live code demos — no slides, no pitches, no fluff.
- Austin AI Developers Group
8,000+ member group hosting deep-dive tech talks on AI, GenAI, LLMs, and Agents plus hands-on code labs.
- Austin Deep Learning
4,000+ practitioners since 2016 — deeply technical talks on computer vision, NLP, and predictive modeling.
- Austin AI / ML / Data Science Meetup
Long-running Austin meetup for AI, machine learning, and computer vision practitioners at all levels.
Conferences & Recurring Events
- Data Science Salon Austin
Annual senior-level ML/AI practitioner conference focused on applied data science in Austin.
- Austin Forum on Technology & Society
Monthly public forum at the Austin Central Library hosting talks on AI, tech policy, and society.
University AI Programs
- Texas AI (UT Austin)
UT Austin's university-wide AI initiative spanning Good Systems, the Oden Institute, and ML Lab.
Industry & Innovation
- Austin AI Alliance
Nonprofit consortium of 120+ members — startups, enterprises, nonprofits, agencies, and UT Austin.
- Capital Factory
Austin's flagship tech hub hosting AI Tinkerers meetups and 100+ developer events per year.
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