Top Natural Language Processing (NLP) Jobs in Austin, TX

Build production AI-powered analytics and natural-language intelligence on governed telematics, aftersales, and energy data. Design multi-agent systems, retrieval/grounding/reasoning/memory patterns, and reusable AI services. Partner with data product teams to ensure trust, explainability, scalability, and performance of AI experiences for enterprise users.

Design, build, evaluate, and deploy NLP and LLM systems (classification, extraction, retrieval). Create evaluation frameworks, golden datasets, and annotation guidelines. Analyze performance, identify failure modes, partner with engineering for production deployment and monitoring, and communicate results to stakeholders.

Design and build agent-based AI features that integrate across BigCommerce and partner systems. Connect agents to MCP servers, enable natural-language store management actions, integrate assistants into the control panel, collaborate with designers and NLP experts, prototype POCs, and contribute to tooling for LLM fine-tuning, prompt engineering, and safe action execution.

Design and deliver LLM-powered, agentic AI systems to automate customer support and compliance. Build scalable, secure backend infrastructure in Python and Go, integrate third-party models/APIs, implement grounding and guardrails, and measure performance. Lead end-to-end projects, mentor engineers, conduct code reviews, and participate in on-call and incident response to improve resolution times and reduce operational costs.

The role involves researching and developing large language models (LLMs) with a focus on transformer architecture, data curation, distributed training, and optimization. Responsibilities include conducting experiments, collaborating with teams, and staying updated on deep learning advancements.

Design and implement Vercel's agentic interface and agent architecture to understand intent, plan workflows, select tools, recover from failures, and execute long-running tasks. Ship polished conversational and interactive experiences across Slack, web, and other surfaces. Build evaluation, observability, and feedback systems and primitives enabling teams to contribute consistent agent capabilities across the company.

Lead end-to-end performance of customer-facing conversational AI and knowledge base. Design, test, and optimize AI conversations, define intents and success metrics, analyze KPIs, maintain enterprise knowledge, run continuous improvement using analytics and experimentation, and partner cross-functionally to improve CX and business outcomes.

Design, research, and deploy NLP/GenAI surveillance models (LLMs, RAG, knowledge graphs) to detect suspicious financial behavior; build production-ready systems, collaborate with Compliance/Legal/IT, and drive adoption of AI solutions.

Own the end-to-end customer-facing conversational agent across chat and voice. Set roadmap, drive conversation and interface design in Figma, prototype with AI coding tools, define agent behavior and quality metrics, partner with enterprise clients for requirements and launches, and iterate from production conversation data.

The Senior Applied Scientist will research and engineer NLP solutions, design algorithms, train LLMs, and contribute to platform development.