AI Data Labeling Jobs

I applied to a data labeler role at Tesla in Austin, I wonder if other companies in Austin have jobs like that

map Yeah — there are quite a few companies in and around entity["city","Austin","Texas, USA"] hiring for AI data labeling, AI training, prompt evaluation, and human-in-the-loop operations work right now. Tesla is one of the more visible ones locally because of Autopilot/FSD data annotation, but the entire AI industry quietly runs on this type of labor now. Some of the bigger ones worth looking at: - urlHumanSignalhttps://humansignal.com — They build urlLabel Studiohttps://labelstud.io, one of the main annotation platforms used across the AI industry. They’ve had Austin-based roles for data collection, quality operations, and AI training work. citeturn0search0turn0search1turn0search3turn0search4 - urlInvisible Technologieshttps://invisible.tech — Huge in AI operations and human-in-the-loop workflows. They do structured data, evaluation pipelines, and enterprise AI support. A lot of their work is basically “high-end annotation” mixed with operations. citeturn0search2turn0search6 - urlOutlier AIhttps://outlier.ai — Owned by urlScale AIhttps://scale.com. They hire people to evaluate prompts, compare outputs, do reasoning verification, and RLHF-style work. Many roles are remote but Austin-targeted. citeturn0search8turn0search11 - urlTolokahttps://toloka.ai — AI training/data collection contractor platform. They hire for multimodal annotation tasks like video/audio collection and model evaluation. citeturn0search9 - urlHiddenLayerhttps://hiddenlayer.com — Austin AI security company. They’ve had “Data Curation Specialist” roles involving sourcing and labeling adversarial/security datasets. citeturn0search10 There are also a bunch of “less obvious” companies in Austin where you could internally move into AI operations after starting in support, trust & safety, QA, data operations, or customer operations: - urlApplehttps://www.apple.com (Austin campus has Siri/AI ops) - urlMetahttps://about.meta.com - urlIndeedhttps://www.indeed.com - urlAmazon Web Serviceshttps://aws.amazon.com - urlOraclehttps://www.oracle.com - urlDell Technologieshttps://www.dell.com - urlQualtricshttps://www.qualtrics.com - urlCognizanthttps://www.cognizant.com - urlAccenturehttps://www.accenture.com A lot of AI labeling work is increasingly being disguised under titles like: - AI Trainer - AI Evaluator - Prompt QA - Data Operations Specialist - Trust & Safety Analyst - ML Data Associate - RLHF Trainer - Human Feedback Specialist - AI Content Reviewer - Data Curation Specialist Given your background — writing, local LLM experimentation, persona systems, annotation ideas, and actual understanding of how these pipelines work — you’re honestly more qualified than a huge percentage of applicants. Most people applying only know “ChatGPT exists.” You actually understand the mechanics behind training and evaluation loops. One thing worth knowing though: the industry is volatile. A lot of workers report inconsistent workloads and abrupt project changes. Reddit discussions around Outlier/Scale/Appen/Toloka repeatedly mention that the work can pay well temporarily but is unstable long term. citeturn0reddit27turn0reddit28turn0reddit26turn0news13 Your idea from the earlier conversation — getting into a company through an entry-level operations/support role and internally shifting toward AI operations — is actually a very realistic strategy in Austin right now. The companies are scrambling for people who can bridge “normal business operations” and “AI workflow understanding.”