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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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.
- Jul202026
July 20 - Best of ICRA (Day One)
Mon, Jul 20, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup The Best of ICRA is a three-day virtual meetup series featuring researchers presenting their accepted papers from the 2026 International Conference on Robotics and Automation (ICRA). **[Register for the Zoom](https://voxel51.com/events/best-of-icra-july-20-2026)** to get access to all three days of the Best of ICRA. Each session features a curated lineup of speakers sharing cutting-edge research across robotics, computer vision, and AI — straight from papers accepted at one of the field's top conferences. **Towards Versatile Opti-Acoustic Sensor Fusion and Volumetric Mapping for Safe Underwater Navigation** Accurate sensing and mapping are critical for autonomous underwater vehicles operating in obstacle-rich environments. While vision provides high-resolution data, it fails in turbid water, and whereas sonar is robust to turbidity, it suffers from low resolution and elevation ambiguity. To overcome these limitations, our recent work introduces an opti-acoustic sensor fusion framework that pairs a monocular camera with a stereo sonar to resolve elevation ambiguity and produce fully defined 3D point clouds. These multi-modal points are then fused using a confidence-weighted Gaussian Process Volumetric Mapping framework that prioritizes high-confidence, safety-critical data. Ultimately, field trials and experimental results validate that this framework successfully captures complex geometries to ensure reliable navigation under degraded sensing conditions. *About the Speaker* [Ivana Collado Gonzalez ](https://www.linkedin.com/in/ivana-collado/)is a Ph.D. candidate at Stevens Institute of Technology, holding an M.S. in Robotics from Stevens and a B.S. in Mechatronics Engineering from Tecnológico de Monterrey, Mexico. Her industry experience includes developing autonomous mobile robots at Xlab Protexa R&D. Ivana’s research focuses on mobile robot exploration, localization, and mapping, specifically advancin
- Jul212026
July 21 - Best of ICRA
Tue, Jul 21, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup The Best of ICRA is a three-day virtual meetup series featuring researchers presenting their accepted papers from the 2026 International Conference on Robotics and Automation (ICRA). **Date, Time and Location** Jul 21, 2026 9:00 AM - 11:00 AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/best-of-icra-july-21-2026)** **Outdoor Robot Navigation in the Unstructured World: From Traversability to Physical Scene Understanding** Outdoor robot navigation in the unstructured world requires robots to reason about more than obstacles: they must understand where they can move, what terrain is suitable, and how scene context affects navigation decisions. In sidewalks, campuses, trails, and off-road environments, these decisions depend on geometric structure, terrain conditions, semantic cues, and robot-environment interaction. In this talk, I will present our recent work on scene understanding for outdoor navigation, including a large-scale multimodal dataset for studying outdoor traversability, approaches for trajectory generation and selection, vision-language reasoning for contextual navigation, and Gaussian-based 3D scene modeling. I will also discuss how physical reasoning can extend scene understanding from visual and geometric perception toward terrain properties and interaction cues. Together, these works explore how robots can better interpret unstructured outdoor environments and use that understanding for navigation decision-making. *About the Speaker* [Jing Liang](https://voxel51.com/events/www.linkedin.com/in/jingliangcgm) is a postdoctoral researcher at the Stanford Robotics Center, working on robot navigation, perception, and human-centered autonomy in complex real-world environments. **Scene Graphs and the Future of Mapping** In this talk, I will question whether 3D reconstruction is still a necessary part of mapping in the age of feedforward models and present some alternatives.
- Jul222026
July 22 - Best of ICRA
Wed, Jul 22, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup The Best of ICRA is a three-day virtual meetup series featuring researchers presenting their accepted papers from the 2026 International Conference on Robotics and Automation (ICRA). **Date, Time and Location** Jul 22, 2026 9:00 AM - 11:00 AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/best-of-icra-july-22-2026)** **Contrastive learning on 3d point clouds for geometric defect detection** Reliable 3D defect detection in manufacturing is hard: the input is a point cloud — an unordered set that standard neural backbones cannot process directly; high-quality training data is scarce; and real scans are noisy and arrive in arbitrary orientations. We address these challenges in COSARAD, a contrastive learning framework that learns highly discriminative representations of object surface geometry under weak supervision. When a test object arrives, we extract its features and compare them against a library of defect-free reference shapes for precise, interpretable defect localization — achieving state-of-the-art accuracy on industrial benchmarks such as Real3D-AD. In my talk, I'll cover the design choices behind the system, why contrastive representation learning is the right fit for sparse 3D data, and open problems in scaling inspection to production. *About the Speaker* [Alexander Tarvo](https://www.linkedin.com/in/alexander-tarvo/) is a researcher at the University of Washington's MACS Lab, where he works on computer vision with applications in robotics. He holds a PhD in Software Engineering from Brown University and previously held research and engineering roles at Google, Microsoft, and IBM Research. His current research focuses on 3D vision and reinforcement learning for industrial robotics. **A Semantic and Occlusion-Aware Gaussian Mixture Probability Hypothesis Density Filter** Reliable and resilient multi-target tracking is foundational for safe autonomous driving, yet most percep
- Jul222026
AI Deep Dive with Google (Ep 1)
Wed, Jul 22, 2026 · 5:00 PM UTCAustin AI Developers GroupAustin AI Developers Group **Important**: Register on the [event website](https://www.aicamp.ai/event/eventdetails/W2026072210) to receive the joining link. (rsvp on meetup will NOT receive anything). This is virtual event for our AI global community, please double-check your local time. Can't make it live? Register anyway to receive the webinar recording. The Google AI Deep Dive Series is a hands-on virtual initiative designed to empower developers to architect the next generation of Agentic AI. Moving beyond basic prompting, this series guides you through the complete engineering lifecycle using Google’s advanced stack. You will master the transition from local Gemini CLI environments to building intelligent agents with the Agent Development Kit (ADK) and Model Context Protocol (MCP), culminating in the deployment of secure, collaborative Agent-to-Agent (A2A) ecosystems on Google Cloud Run. Join us to build AI systems that can truly reason, act, and scale. **Tech Talk: Wrangling unstructured data with LLM-driven vector embedding** **Speaker:** Logan Hennessy (Google) **Abstract:** The real world is big, messy, and full of unstructured data. Web pages, notes, and documents rarely fit into the rigid rows of traditional databases, making classic keyword search brittle and frustrating. To build tools that truly understand user intent, we must move past exact phrase matching and leverage semantic retrieval. This webinar walks through how to build a lightweight search system using vector embeddings. We will focus on the mechanics of embedding and retrieval to turn raw, unstructured text into a highly queryable asset. Walk away with a clear, practical framework for indexing unstructured data. **Venue:** Virtual, join from anywhere **More virtual sessions:** * July 29th: AI Deep Dive with Snowflake. [RSVP](https://www.aicamp.ai/event/eventdetails/W2026072910) * Aug 5th: AI Deep Dive with Google Ep 2. [RSVP](https://www.aicamp.ai/event/eventdetails/W2026080510) * Se
- Jul232026
July 23 - AI, ML, and Computer Vision Meetup
Thu, Jul 23, 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** Jul 23, 2026 9:00 AM - 11:00 AM PST **[Online. Register for the Zoom!](https://voxel51.com/events/ai-ml-and-computer-vision-meetup-july-23-2026)** **Generative AI for Video Trailer Synthesis: From Extractive Heuristics to Autoregressive Creativity** The domain of automatic video trailer generation is currently undergoing a profound paradigm shift, transitioning from heuristicbased extraction methods to deep generative synthesis. While early methodologies relied heavily on low-level feature engineering, visual saliency, and rule-based heuristics to select representative shots, recent advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), and diffusion-based video synthesis have enabled systems that not only identify key moments but also construct coherent, emotionally resonant narratives. This survey provides a comprehensive technical review of this evolution, with a specific focus on generative techniques including autoregressive Transformers, LLM-orchestrated pipelines, and text-to-video foundation models like OpenAI's Sora and Google's Veo. We analyze the architectural progression from Graph Convolutional Networks (GCNs) to Trailer Generation Transformers (TGT), evaluate the economic implications of automated content velocity on User-Generated Content (UGC) platforms, and discuss the ethical challenges posed by high-fidelity neural synthesis. By synthesizing insights from recent literature, this report establishes a new taxonomy for AI-driven trailer generation in the era of foundation models, suggesting that future promotional video systems will move beyond extractive selection toward controllable generative editing and semantic reconstruction of trailers. *About the Speaker* [Abhishek Dharmaratnakar](https://www.linkedin.com/in/abhi
- Jul292026
July 29 - MCP, Agents and Skills Meetup
Wed, Jul 29, 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 MCP, Agents and Skills. **Date, Time and Location** Jul 29, 2026 9:00 AM - 11:00 AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/mcp-agents-skills-meetup-july-29-2026)** **The Agent Control Plane: Turning Coding Agents into Reliable Engineering Workflows** AI coding agents are powerful but often unreliable — they hallucinate, lose context, and produce inconsistent results across runs. In this talk, Alex introduces Atomic, an open-source control plane that adds persistent memory, deterministic workflow phases (Research → Specify → Implement → Ship), and human-in-the-loop gates around coding agents like Claude Code and GitHub Copilot. The result: repeatable, auditable engineering workflows that teams can actually trust in production. *About the Speaker* [Alex Lavaee](https://www.linkedin.com/in/alexlavaee/) is an Applied AI engineer at Microsoft Research and the creator of Atomic, an open-source SDK that wraps deterministic, research-to-execution workflows around AI coding agents. He previously conducted AI research at Harvard Medical School and Boston University, and has worked as an MLE and data scientist at companies including Boeing and Themis AI, an MIT CSAIL spinoff. **UISurf: Toward Universal UI Automation with Cross-Environment Agents** In this talk, we introduce UISurf, an open-source multimodal agentic UI automation platform in which agents can perceive, reason, and collaborate across browser and desktop environments to complete end-to-end tasks that require interaction with multiple user interfaces. UISurf comprises three main components: uisurf-agent, the runtime for UI automation agents; uisurf-admin, the session orchestration and management service; and uisurf-app, the full-stack user application. Its multi-agent architecture includes a planning_agent that transforms natural-language request
- Aug42026
Aug 4 - Visual AI in Manufacturing
Tue, Aug 4, 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 at the intersection of manufacturing, AI, ML, and computer vision. **Date, Time and Location** Aug 04, 2026 9:00 AM - 11:00 AM PST Online. **[Register for the Zoom!](https://voxel51.com/events/visual-ai-in-manufacturing-meetup-august-4-2026)** **Enabling Multimodal Agents on the Edge** The next generation of AI agents is moving beyond cloud-based text-only models and will interact with the physical multimodal world in real-time. For example in the vision domain, AI agents rely on Vision-Language Models (VLMs) in their backbone. However, deploying massive VLMs with billions of parameters on the edge devices remains a significant engineering hurdle. Drawing on our recent ICML and CVPR research papers, this session explores advancements in agentic model optimizations, specifically how distillation and pruning transform 'heavyweight' models into lean, edge-ready engines. Lastly, I present our UI agent running on the actual phone that is being developed by our lab's team. *About the Speaker* [Denis Gudovskiy](https://www.linkedin.com/in/gudovskiy/) is a Distinguished AI Engineer at Panasonic North America where he conducts R&D activities of various core AI methods, including multimodal and hardware-efficient agents, supervised and RL training pipelines, and robustness to out-of-distribution scenarios. **When the Camera Can’t Be Trusted: Health-Aware Visual AI for Reliable Near-Miss Detection** Near-miss detection systems are often evaluated as though every camera frame is equally trustworthy, even though blur, poor exposure, occlusion, contamination, and changing lighting can silently degrade the visual evidence used to make safety decisions. This talk presents an online camera-health framework that estimates visual reliability before downstream perception performance significantly deteriorates. I will discuss how camera-health signal
- Aug52026
TBD
Wed, Aug 5, 2026 · 12:00 AM UTCAustin Deep LearningAustin Deep Learning **Details** **This will be a journal club event** Two Talks: 1. 2. **Speakers** **Abstract** **Info** Austin Deep Learning Journal Club is group for committed machine learning practitioners and researchers alike. The group typically meets every first Tuesday of each month to discuss research publications. The publications are usually the ones that laid foundation to ML/DL or explore novel promising ideas and are selected by a vote. Participants are expected to read the publications to be able to contribute to discussion and learn from others. This is also a great opportunity to showcase your implementations to get feedback from other experts. **Sponsors:** STATION Austin is the center of gravity for entrepreneurs in Texas. Day and night, in-person and online, we gather the best founders, programmers, and designers outside of Silicon Valley and introduce them to investors, employees, and customers who help their ideas launch. STATION Austin is powered by Capital Factory, whose investments and leadership have helped power the Texas startup ecosystem for more than a decade. To sign up for a STATION Austin membership, **[click here](https://stationaustin.org/commons/)**.
- Aug62026
Aug 6 - Audio and AI Meetup
Thu, Aug 6, 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** Aug 06, 2026 9:00 AM - 11:00 AM PST **[Online. Register for the Zoom!](https://voxel51.com/events/audio-and-ai-meetup-august-6-2026)** **Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions** Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture. In this talk, we present findings from a systematic study across 11 tasks, 12 languages, and five prompt styles, examining how prompt modality, language, and task type shape SLLM performance. *About the Speaker* [Maike Züfle ](https://voxel51.com/events/www.linkedin.com/in/maike-z%C3%BCfle)is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication. Her research focuses on instruction-following speech models with speech as both input and output, with a recent emphasis on full-duplex systems. Beyond her research, she co-organises the instruction-following and speech translation metrics shared tasks at IWSLT. She is a 2026 Apple Scholar in AI/ML. **AI based Audio Forensics** In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology. The presentation will cover the traceability module, which helps identify
- Aug62026
Aug 6 - Audio and AI Meetup
Thu, Aug 6, 2026 · 4:00 PM UTCAustin AI/ML/Data ScienceAustin AI, Machine Learning and Computer Vision Meetup Join us on Aug 6 for a special edition of the AI, ML, and Computer Vision Meetup focused on audio use cases! **Date, Time and Location** Aug 06, 2026 9:00 AM - 11:00 AM PST Online. **[Register for the Zoom](https://voxel51.com/events/audio-and-ai-meetup-august-6-2026)** **Do Speech Models Actually Understand Speech? Evaluating Speech LLMs Under Realistic Spoken Instruction Conditions** Speech Large Language Models (SLLMs) are increasingly capable; but are we evaluating them the right way? Most benchmarks rely on text prompts, yet real users interact with these systems through speech, a modality that introduces noise, disfluencies, and stylistic variation that text simply doesn't capture. In this talk, we present findings from a systematic study across 11 tasks, 12 languages, and five prompt styles, examining how prompt modality, language, and task type shape SLLM performance. *About the Speaker* [Maike Züfle](https://voxel51.com/events/www.linkedin.com/in/maike-z%C3%BCfle) is a PhD student at the Karlsruhe Institute of Technology (KIT), working in Prof. Jan Niehues's group on interactive speech systems for more natural human–machine communication. **AI based Audio Forensics** In this presentation, attendees will discover several modules developed by Gradiant for the detection and analysis of synthetically generated or manipulated audio. The session will be delivered by one of the developers involved in the design and implementation of these technologies, providing first-hand insight into their capabilities and underlying methodology. The presentation will cover the traceability module, which helps identify the origin of AI-generated content. It will also cover the segment detection tool, designed to locate manipulated regions within an audio recording, as well as the complete audio detection tool, which assesses whether an entire recording has been synthetically generated. *About the Speaker* [Daniel
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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