10 open roles · refreshed hourly
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.
Latest roles
Latest Austin AI roles
Cybersecurity AI Forward Deployed Engineer - FDE - Consultant
10/13Accenture • Morristown +41
Claude CodeCursorGithub Copilot+30+29+28$59k – 235kAI Software Engineer
10/10Gallatin AI, Inc. • Austin, TX
PythonC++C+27+26+25$80k – 210kSenior Full Stack Engineer -AI Native Seed Startup
10/9Benchstack Ai • Austin, TX
JavaScriptModern Javascript FrameworksPython+13+12+11$200k – 225kSenior Solutions Architect
10/6Databricks • Austin, TX
PythonSQLApache Spark+15+14+13$219k – 301kAgentic AI Engineer, Vice President
10/6State Street • Austin, TX
PythonJavaGo+24+23+22$120k – 218kRobotics Engineer
10/5Loombotic • Austin, TX
RoboticsRobot ProgrammingEnd Effector Programming+13+12+11$100k – 160k- GM
Staff AI/ML Engineer, Developer Productivity
10/4General Motors • Austin, TX
GoPythonUnix/linux+13+12+11$180k – 284k Security AI Ops Engineer
10/4Thales • Austin, TX
Amazon BedrockAmazon Opensearch ServiceAWS+25+24+23$116k – 215kSr. Forward Deployed Engineer (FDE) - Retail
9/21Databricks • Austin, TX
PythonScalaJavaScript+23+22+21$182k – 250k
Career guide
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.
Events
Upcoming AI events in Austin
AI meetups, hackathons and conferences coming up in the area, refreshed from organizer calendars.
- Oct22Oct 22 - Advances in AI at Virginia TechThu, Oct 22 · 4:00 PM UTCAustin AI, Machine Learning and Computer Vision Meetup Join our virtual meetup to hear talks from AI researchers at Virginia Tech! Date, Time and Location Oct 22, 2026 9:00 AM - 11:00 AM PST Online. Register for the Zoom! Multi-Agent Communication: A framework, diagnostic and mechanistic perspective Multi-agent LLM systems are increasingly used for collaborative reasoning, debate, and consensus, yet their communication dynamics remain poorly understood. This talk presents a framework for studying multi-agent communication through diagnostic and mechanistic perspectives. I will discuss CONSENSAGENT, which improves consensus by mitigating sycophancy, alongside our diagnostic work on communication patterns and failure modes in real-world multi-agent debates. I will then present ongoing work that moves toward a mechanistic understanding of how these interaction patterns arise internally, with the broader goal of making multi-agent systems more interpretable, reliable, and controllable. About the Speaker Priya Pitre I am an Ph.D student in the Computer Science Department at Virginia Tech (VT), co-advised by Dr. Xuan Wang and Dr. Naren Ramakrishnan. Exposing and Improving Fine-Grained Visual Grounding Abilities of Lightweight Multimodal LLMs Lightweight multimodal LLMs can localize whole objects effectively, yet often struggle when a query targets a small object part or fine-grained visual detail. This talk presents a reasoning-guided framework that teaches compact models to ground parts through an explicit coarse-to-fine process: first locating the parent object, then identifying the requested part. A part-aware reinforcement-learning objective provides stage-wise rewards for object accuracy, part containment, and the consistency of the model’s self-critique. Using these techniques, a compact 4B-parameter model achiev
- Oct28Coder Agentic AI MeetupWed, Oct 28 · 10:30 PM UTCAustin AI Developers Group Important: Register on the event website is required for admission. 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. 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 update \ 6:15pm\~8:00pm: Tech talks and Q&A \ 8:00pm\~8:30pm: Open discussion & Mixer Tech Talk: Building Functional Dev Environments for Humans and Agents Speaker: Ben Potter, Head of DevRel. @ Coder Tech Talk: Don’t Blow Your Token Budget Being an Agent Psychologist Speaker: Roni Dover, Head of AI Labs, JetBrains. Topics/Speakers: Stay tuned as we are updating speaker/topics. If you have a keen interest in speaking to our community, we invite you to submit topics for consideration: Submit Topics Sponsors: We are actively seeking sponsors to support AI developers community. Whether it is by offering venue spaces, providing food, or cash sponsorship. Sponsors will not only speak at the meetups, receive prominent recognition, but also gain exposure to our extensive membership base of 8,000+ AI developers in Austin and 500K+ worldwide.
- Oct29Oct 29 - How to Build Vision Data Agents with Tools, Skills, and MCPThu, Oct 29 · 4:00 PM UTCAustin 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). Date, Time and Location Oct 29, 2026 9:00 AM - 10:00 AM PST Online. Register for the Zoom! 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.
- Nov4TBDWed, Nov 4 · 1:00 AM UTCAustin 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.
- Nov5Nov 5 - Visual AI Agriculture MeetupThu, Nov 5 · 5:00 PM UTCAustin AI, Machine Learning and Computer Vision Meetup Join our virtual meetup to hear talks from experts on cutting-edge topics at the intersection of agriculture and AI. Date, Time and Location Nov 05, 2026 9:00 AM - 11:00 AM PST Online. Register for the Zoom! Talks will include: U-Net Framework for Micro-Scale Surface Damage Segmentation in High-Resolution Soybean Seed Imagery Accurate detection of surface-level seed damage is critical for soybean seed quality assurance, yet automated micro-scale damage detection in high-resolution imagery remains an open challenge due to extreme spatial variability in damage scale, severe class imbalance across damage types, and the computational demands of processing ultra-high resolution agricultural imagery at scale. This research addresses the semantic segmentation of fine-grained soybean seed surface defects, such as wrinkles, dark spots, and general surface damage, in 6048 × 4024 pixel images where target damages can be as small as 18 × 18 pixels, using an augmented dataset of 77,000 individual seed images. To overcome the difficulties of micro-scale detection, spatial sparsity, and class confusion, we propose a two-stage training and dual model inference framework built on an optimized U-Net architecture with a ResNet34 encoder. In the first stage, a damage specialist model is trained using weighted loss functions and a class-balanced approach that prioritizes damage classes. In the second stage, transfer learning is applied to initialize a healthy seed specialist model from Stage 1 weights, with rebalanced class weights and tile probabilities that identify healthy seeds. During inference, a confidence-gated damage filter suppresses low-confidence predictions, and healthy seed labels are assigned only when the specialist model's confidence exceeds that of the damage model. The two specialist models achieve validation accuracies
- Nov11Nov 11 - AI, ML and Computer Vision MeetupWed, Nov 11 · 5:00 PM UTCAustin 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. Time, Date and Location Nov 11, 2026 9:00 AM - 11:00 AM PST Online. Register for the Zoom! Agentic RAG: Beyond Retrieve-and-Generate Retrieval-Augmented Generation (RAG) has become the default pattern for grounding large language models in external knowledge, but most implementations still follow a rigid retrieve-once, generate-once pipeline — one that struggles with multi-hop questions, ambiguous queries, and knowing when its own retrieved context is insufficient. This talk introduces Agentic RAG, where retrieval is treated as an action within an agent's reasoning loop rather than a fixed upstream step. We'll examine query decomposition and routing for breaking complex questions into targeted sub-queries, self-reflective and corrective retrieval loops that let an agent judge and re-query its own results, and tool-orchestration patterns (via MCP) that let retrieval sit alongside other agent actions like database lookups and API calls. Using a live architecture — evolving a standard RAG chatbot into an agentic, MCP-connected system — we'll walk through what changes in design, and where these systems introduce new failure modes: grounding drift, latency and cost from repeated retrieval loops, and cases where a simpler RAG pipeline still wins. Attendees will leave with a practical framework for deciding when the added complexity of agentic RAG is worth it, and a set of design patterns for building it correctly. About the Speaker Balaji Venkatasubramaniyar is a Technical Lead at Wisdom Infotech, leading a 15+ person engineering team delivering enterprise solutions. With 13+ years of experience in enterprise software and insurance technology, he speci
- Nov19Nov 19 - Visualizing and Debugging Hugging Face's LeRobot DataThu, Nov 19 · 5:00 PM UTCAustin AI, Machine Learning and Computer Vision Meetup This workshop is a hands-on tour of what actually breaks when you load real LeRobot datasets, and how Voxel51's native LeRobot support in FiftyOne catches it. Date, Time and Location Nov 19, 2026 9:00 AM - 10:00 AM PST Online. Register for the Zoom! Robot learning data doesn't look like the data that trains most vision-language models. It's temporal, low-dimensional in the parts that matter most, embodiment-specific, and expensive to collect, and increasingly, it's shipped in Hugging Face's LeRobot format. But packaging robot data consistently isn't the same as being able to see it: episodes hide broken metadata, silently dropped sensor modalities, and mismatched schemas between robots that look identical on paper. Starting from the core differences that make robot data hard (temporal structure, proprioception, action grounding, embodiment heterogeneity, data scarcity), we'll move into live debugging: a metadata bug that broke multiple dataset imports, sensor streams that vanish without an error, and two robots running an identical benchmark whose data is structurally incompatible. By the end, you'll know what to check before you trust a LeRobot dataset, and how to actually look inside one.
- Dec10Dec 10 - AI, ML and Computer Vision MeetupThu, Dec 10 · 5:00 PM UTCAustin 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 Dec 10, 2026 9:00 AM - 11:00 AM PST Online. Register for the Zoom! Your mAP is fine. Your slices are not. Finding the images that actually matter A single mAP can look acceptable while one slice is unusable, small objects, occlusion, or a confused class. I will run a pretrained detector on a few hundred public images (COCO-style, not a production crawl), report the headline metric, then break the same run by slice and walk through the worst samples. The claim is modest: you do not need a million images or a trained-from-scratch model to see what the mean is hiding, and you cannot relabel everything, so the next labeling budget should follow those slices. No employer data and no confidential dashboards. About the Speaker Amandeep Jiddewar is a Machine Learning Engineer at Pinterest, with prior work in U.S. manufacturing analytics (CertainTeed / Saint-Gobain) and marketplace allocation (QuinStreet). Emory Goizueta MSBA (2019), Kaggle Expert. This talk is in his personal capacity and does not represent his current or prior employers. Production AI Agents: Tool Use, Evaluation, Guardrails and Observability Building an AI agent is easy. Knowing whether it is behaving reliably in production is much harder. This session explores how to evaluate agents that use tools and external systems, including tool-call validation, tracing, observability, failure analysis, guardrails, permissions, and human review. Attendees will learn practical patterns for understanding not just what an agent answered, but why it acted and whether the action was safe. About the Speaker Akshay Talathi is a Vice President a
Community
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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