Top AI Engineer Jobs in Austin, TX

Evaluate AI coding-agent interactions for usefulness, correctness, engineering judgment, explanations, and trustworthiness. Provide clear written and video feedback, distinguish response quality levels, and help define standards for tools such as Codex, Claude Code, and Cursor. The role requires senior engineering experience, strong TypeScript/JavaScript or Python skills, familiarity with AI-assisted development, excellent English communication, and comfort making rigorous subjective judgments.

Evaluate AI coding agents such as Codex, Claude Code, and Cursor for engineering quality, reasoning, usefulness, and judgment. Provide rigorous written feedback, distinguish response quality levels, assess explanations without executing every line, and help define standards for effective AI-assisted development workflows. The role is remote, flexible, contract-based, and requires strong communication skills and senior-level software engineering experience.

Evaluates AI coding-agent interactions for technical quality, engineering judgment, reasoning, explanations, and user trust. Provides detailed written and video feedback, distinguishes response quality levels, and helps define standards for tools such as Codex, Claude Code, and Cursor. This flexible, remote contract requires senior-level software engineering experience, strong TypeScript/JavaScript or Python skills, familiarity with AI-assisted development, and excellent English communication.

Set technical direction for Root’s AI platform, including infrastructure, tooling, shared services, governance, reliability, security, observability, and cost management. Lead cross-team initiatives, guide AI and LLM architecture decisions, establish operating standards, shape technical roadmaps, resolve complex tradeoffs, and mentor engineers and technical leaders across the organization.

Design, build, evaluate, and productionize machine learning and LLM-based agentic systems for healthcare products. Own projects end-to-end, including problem framing, dataset development, modeling, evaluation, deployment, monitoring, and safety guardrails. Build ML infrastructure, data pipelines, inference systems, and evaluation frameworks. Collaborate with clinicians, product managers, and engineers, while setting technical direction and mentoring team members.

Lead technical vision and architecture for AI frameworks, performance, benchmarking, and tooling. Drive cross-team alignment, mentor engineers, ship high-performance distributed software, and improve model onboarding, runtime performance, reliability, and Azure hardware efficiency.

Designs and develops secure, performant Copilot products and platform services across Windows, Mac, and mobile. Builds distributed AI and agent-driven systems capable of reasoning, planning, and executing tasks. Collaborates with infrastructure, platform, application engineering, and research teams to ship high-quality software. Champions AI-assisted development, coding-agent adoption, prompt design, tooling best practices, and human-in-the-loop systems.

As a Staff AI/ML Engineer, you'll enhance developer productivity, mentor engineers, drive best practices, and implement scalable solutions for AV tools and services.

Leads architecture and hands-on development for AI frameworks, performance systems, benchmarking, and developer tools. Investigates issues across models, compilers, runtimes, services, and accelerator hardware; improves model onboarding, runtime performance, reliability, hardware utilization, and Azure efficiency. Establishes scalable measurement and observability capabilities, influences cross-team technical strategy, delivers production solutions, and mentors engineers while advancing engineering quality and maintainability.

Provides technical leadership for AI framework and model-serving initiatives at cloud scale. Owns architecture, roadmaps, performance optimization, benchmarking, observability, and multi-release execution across models, compilers, runtimes, services, GPUs, and silicon. Leads cross-team alignment, makes architectural decisions, mentors engineers, performs hands-on implementation and debugging, and drives measurable improvements in model onboarding, runtime performance, reliability, hardware utilization, and Azure capacity efficiency.

Conduct research on frontier AI systems, including synthetic and agentic data generation, reinforcement learning, post-training, model understanding, benchmarking, and evaluation. Design rigorous experiments, datasets, prototypes, tooling, and research workflows; train and assess models; analyze results; and translate findings into scalable AI products. Collaborate across research, engineering, product, and operations teams, communicate technical conclusions, contribute to publications or open-source work, and mentor engineers and researchers.

Build and ship production-grade AI features by embedding with clinical, product, and operations teams. Own solutions end to end, including architecture, evaluation, validation, governance, deployment, monitoring, and human-in-the-loop safeguards. Develop agentic capabilities using tool calling, RAG, MCP integrations, and orchestration on AWS Bedrock. Establish acceptance criteria and risk controls, contribute reusable platform assets, and improve AI fluency across teams. The role requires strong Python, cloud engineering, full-stack development, and experience delivering validated AI features in production.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

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.

Build and operate full-stack, AI-powered applications and agentic systems for enterprise customer experiences. Responsibilities include developing React and TypeScript frontends, backend services, LLM integrations, tool-using agents, evaluation frameworks, and AWS-hosted services. The role owns features end to end, translates ambiguous customer problems into products, prototypes rapidly, and improves system reliability, safety, observability, cost, latency, and robustness in production.

Develop production software using AI-first engineering practices. Translate requirements into prompts, implementation plans, and acceptance criteria; generate and validate AI-assisted code; identify hallucinations, security issues, and logic gaps; create automated tests; and ensure architecture, performance, documentation, and production readiness. Build reusable prompt workflows, collaborate with product and technical teams, document decisions, and guide engineers in adopting AI-native development practices.

Own and evolve MAX Framework APIs and developer experience for AI inference and training. Design scalable APIs, programming models, distributed execution primitives, and developer-facing abstractions across Python and Mojo-adjacent surfaces. Lead RFCs and technical specifications, build reference implementations, establish API quality standards, and collaborate with compiler, runtime, kernels, serving, documentation, and DevRel teams.

Lead end-to-end AI improvement initiatives by assessing workflows, identifying automation opportunities, gathering stakeholder requirements, designing and integrating AI-powered solutions, and managing deployment and adoption. The role also includes process redesign, executive communication, internal AI enablement, responsible AI governance, privacy, access controls, and measuring post-implementation business impact.

Lead the design and delivery of production AI systems, agentic workflows, and shared AI platform capabilities for loan origination and servicing. Responsibilities include model selection and routing, MCP and agent interfaces, RAG and context engineering, evaluation and observability, reliability practices, and scalable backend architecture. The role operates independently on complex initiatives, establishes technical patterns, makes platform decisions, and mentors engineers.

Build and operate production-quality AI framework, runtime, benchmarking, performance, automation, observability, and developer-tooling systems. Optimize large language model training and inference across GPUs and Microsoft hardware, diagnose cross-stack reliability and performance issues, and improve model onboarding, hardware utilization, Azure efficiency, and deployment speed. Senior engineers own major components and projects; Principal engineers define technical direction, architecture, and multi-release strategy while leading cross-team initiatives and mentoring technical leaders.

Design, build, and productionize LLM-powered systems (adaptive content, tutors, evaluators) and AI pipelines (RAG, embeddings, fine-tuning). Prototype and optimize models across modalities, define AI safety/evaluation/observability practices, and collaborate with product and engineering to measure learner outcomes and iterate.

Develop and implement AI-driven applications, workflows, integrations, and ServiceNow solutions across modules including ITSM, ITOM, CSM, and HRSD. Gather requirements, write and test code, configure REST/SOAP integrations, implement CMDB and Discovery capabilities, support impact mapping and event management, promote changes across environments, and remediate production incidents. The role also applies ServiceNow AI features such as Virtual Agent, Predictive Intelligence, Performance Analytics, and Now Assist.

Design, build, and maintain AI agents and automated marketing workflows. Map automation opportunities, deploy and monitor agents, integrate with marketing tools, produce impact reporting, ensure data governance, and document/enable marketing teams.

Leads the implementation of complex software systems and services, influences architecture and modernization, mentors engineers, and supports production reliability. Partners with Product, QA, and DevOps teams to deliver scalable cloud-native applications. The role requires strong full-stack expertise across C#, .NET, React, TypeScript or JavaScript, SQL, APIs, distributed systems, CI/CD, and AI development methodologies, along with the ability to independently lead complex technical initiatives.

Design, build, and deploy production-grade agentic AI systems, including autonomous and multi-agent architectures, tool-use orchestration, memory, retrieval, and function-calling workflows. Integrate agents with enterprise tools and databases, implement evaluations, guardrails, observability, fallback paths, and human-in-the-loop controls. Lead technical strategy, client collaboration, prototyping, and product direction for enterprise AI initiatives.