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 10 of 10 opportunities
AI Engineer, Advanced Solutions Lab, Google Cloud
Robotics Engineer, Senior
Embedded Engineer (BSP), Staff
Freelance Annotator (English) - AI Trainer
Senior Mechatronics Engineer
Lead Mechatronics Engineer
Machine Learning Engineer, Senior
Computer Vision Engineer, Senior
Perception Engineer, Staff
Robotics Engineer, Staff
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.
- 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
- Jun302026
June 30 - Beyond Annotation Tools: Building a Complete Physical AI Data Engine
Tue, Jun 30, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup In this workshop we’ll demonstrate workflows for image and video annotation, instance segmentation, polylines, QA and review, collaborative labeling operations in [FiftyOne](https://docs.voxel51.com/), and smart data selection strategies that help teams reduce wasted labeling spend. **Date, Time and Location** Jun 30, 2026 9 AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/beyond-annotation-tools-building-a-complete-physical-ai-data-engine-with-fiftyone-june-30-2026)** Annotation is no longer just about drawing boxes. Modern physical AI teams need an end-to-end system for labeling, QA, dataset curation, project management, auto-labeling, and video understanding — all tightly integrated into the workflows where models are actually built and evaluated. You’ll also get an early look at new agentic labeling workflows powered by “Labeling Agents” — intelligent systems that can learn from text prompts and visual examples to automatically label datasets at scale. We’ll walk through how teams can rapidly create reusable labeling agents, validate outputs, and apply them across large datasets as background tasks. Whether you’re building computer vision models for robotics, autonomous systems, manufacturing, retail, or multimodal AI applications, this session will show how integrated annotation and data-centric workflows can dramatically accelerate iteration speed while improving dataset quality. **What You’ll Learn** * How smart data selection strategies reduce annotation costs and improve model performance * Why integrated annotation is becoming a core requirement for modern physical AI platforms * How to unify data curation, annotation, evaluation, and model iteration inside a single workflow * How FiftyOne supports annotation workflows for Classification, Object detection, Instance segmentation, Polylines, Video detection and tracking * How to create, edit, QA, and manage 2D and 3D labels dir
- Jul12026
July 1 - Getting Started with FiftyOne
Wed, Jul 1, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup This workshop is part of our Getting Started with FiftyOne monthly series — a recurring session designed to help you build a strong foundation in data-centric AI workflows. **Time, Place and Location** July 1, 2026 9 AM PST - 10 AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/getting-started-with-fiftyone-july-1-2026)** In this session, you’ll learn how to manage large-scale computer vision datasets using open source FiftyOne. We’ll cover how to curate, visualize, and evaluate your data and models — with a focus on improving data quality over brute-force model iteration. You’ll walk away with a repeatable framework for building data-centric AI pipelines across research and production. **What you’ll learn:** * Structure unstructured data into queryable schemas (images, video, point clouds) * Query datasets using the FiftyOne SDK with filters, tags, and confidence thresholds * Visualize high-dimensional embeddings to identify clusters, gaps, and outliers * Automate data curation and prioritize high-value samples for labeling * Debug model performance using evaluation tools (confusion matrices, PR curves) * Customize FiftyOne with dashboards and interactive panels **Prerequisites:** * Working knowledge of Python * Familiarity with machine learning and/or computer vision fundamentals
- Jul12026
AI Agent Security Documentary Premiere
Wed, Jul 1, 2026 · 10:30 PM UTCAustin AI Developers GroupAustin AI Developers Group This is exclusively invited event, please apply to attend on the [event website](https://www.aicamp.ai/event/eventdetails/W2026070116) REQUIRED for admission. The event is designed for Senior AI Developers and Tech Leaders, such as CTOs, CIOs, AI architects, and senior leaders making decisions about AI adoption and security. **THE DOCUMENTARY** Before this documentary is released to the world, we are bringing together a select group of senior AI leaders for an exclusive premiere screening. AI agents are being deployed faster than they are being secured. 88% of organisations have already experienced a confirmed or suspected AI agent security incident — yet only 14% have full security approval for their agent fleet. This documentary confronts that gap head on. Hosted by Alex Kantrowitz — founder of Big Technology, the film brings together four of the most important voices on AI agent security working today. * Ramesh Raskar , MIT Associate Professor — what makes AI agents uniquely dangerous and what the speed of deployment actually means for security. * Theresa Payton , former White House Chief Information Officer — why this is not just an enterprise risk problem and what the broader stakes are. * Sharon Gai , ex Alibaba - what innovation is happening in enterprises and scale ups. * Rory Blundell , CEO of Gravitee — what organisations can actually do about it right now. All captured by an Emmy award winning director. **The EVENT** This is an invite-only evening. A red carpet champagne reception, a private screening of the documentary, a Q&A, and a conversation worth having. The decisions being made about AI agent security right now will matter for years. **WHO SHOULD ATTEND:** CTOs, CIOs, AI architects, and senior leaders making decisions about AI adoption and security. **AGENDA:** * 6:00 Red carpet champagne reception * 7:00 Screening (25 minutes) * 7:25 Q&A (30 minutes) * 7:55 Networking * 9:00 Closing **Q&A SPEAKERS:** Lessons Fr
- Jul82026
July 8 - Best of CVPR (Day 1)
Wed, Jul 8, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup Welcome to the Best of CVPR series — your virtual front row to groundbreaking research, insights, and innovations from one of computer vision's premier conferences. Live from the authors to you. **Date, Time and Location** Jul 08, 2026 9 AM - 11 AM PT Online. **[Register for Zoom!](https://voxel51.com/events/best-of-cvpr-july-8-2026)** **Some Modalities Are More Equal Than Others: Understanding and Improving Multimodal Integration in MLLMs** Multimodal large language models can process vision, audio, and text, but it remains unclear whether they truly integrate these modalities or rely on shortcut cues. In this talk, I will present our recent work, [“Some Modalities Are More Equal Than Others,](https://arxiv.org/abs/2511.22826)” where we introduce MMA-Bench, a benchmark designed to probe MLLMs under controlled audio–visual conflict, misleading text, and modality-specific queries. Through black-box evaluation and white-box attention analysis, we show that current MLLMs often struggle when modalities disagree, exhibit model-specific modality biases, and can be distracted by irrelevant textual context. We further propose an alignment-aware tuning strategy that trains models to answer based on the queried modality, improving robustness and multimodal grounding. This talk will highlight both the failure modes of current MLLMs and practical directions toward more reliable cross-modal reasoning. *About the Speaker* [Tianle Chen](https://www.linkedin.com/in/tianle-chen-1811341b7/) is a Ph.D. student in Computer Science at Boston University, advised by Prof. Deepti Ghadiyaram. His research focuses on multimodal large language models, audio–visual reasoning, robustness, and trustworthy multimodal AI. He is interested in understanding how models allocate evidence across modalities and designing methods that improve reliable multimodal reasoning. **LinkedOut: Linking World Knowledge Out of Video LLMs for Next-Generat
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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