Top Tech Jobs & Startup Jobs in Austin, TX

Design and deliver enterprise-grade AI workflow transformations on GCP. Build secure, event-driven backend services and multi-step AI agents, integrate bots with enterprise systems, implement telemetry, testing, CI/CD, HITL validation, and create reusable architecture, documentation, and runbooks while guiding adoption and governance.

Design, build, and scale AI-enabled workflow and automation solutions across collaboration platforms and enterprise systems. Translate manual processes into secure, reusable, event-driven automations using Python/JavaScript, integrate AI capabilities like Gemini, implement logging and telemetry, and partner with product, security, and business teams to deploy and sustain enterprise transformation solutions.

Build and maintain master data, metadata, repositories, logical data models, and data standards. Assemble complex datasets, improve data processes and infrastructure scalability, and develop industrialized analytical datasets and delivery mechanisms. Use data pipelines to generate insights into customer acquisition, operational efficiency, and business performance metrics. This is a hybrid role requiring three days in the office and two days remotely.

Lead AI, machine learning, and data science workstreams from methodology through production delivery. Develop scalable, maintainable analytical code, explore new methods, influence product decisions, and collaborate with engineering, product, business, and data teams. Translate technical tradeoffs into actionable recommendations, present findings to technical and business audiences, and deliver measurable business impact while managing risks and maintaining methodological quality.

Lead development of scalable, reliable continuous integration infrastructure supporting autonomous-vehicle development, machine learning training, simulation, and remote builds. Specialize in Remote Build Execution and a FUSE-based file system, enabling source-code editing and developer workflows. Design and implement productivity improvements, evaluate technologies, influence technical roadmaps, establish engineering best practices, manage technical debt, and mentor engineers while balancing business and customer priorities.

Lead architecture and development of GM’s in-vehicle AI assistant platform on Android Automotive OS. Build scalable APIs, SDKs, edge-cloud integrations, CI and automated validation infrastructure, and robust AIDL/IPC application contracts. Guide engineering execution, optimize latency and memory, integrate AI/ML evaluation pipelines, and collaborate with product and UX teams. The role requires extensive Android architecture experience, technical leadership, Kotlin and Java expertise, and familiarity with distributed client-server systems.

Leads the architecture and development of scalable backend APIs, microservices, distributed systems, data pipelines, and integrations for vehicle commerce platforms. Responsibilities include modernizing legacy systems, implementing automation, writing secure and tested code, mentoring engineers, evaluating technologies, and championing CI/CD, observability, and performance improvements. The role requires expertise in Java, Spring Boot, Kafka, cloud-native infrastructure, Azure, containers, databases, and generative AI technologies.

Lead full-lifecycle software development integrating robots and plant-floor devices with manufacturing data systems. Build, maintain, debug, optimize, and secure production applications using C++, Java, Azure, ROS, testing, CI/CD, and agile practices. Collaborate with manufacturing, controls, OT/IT, and data teams; support on-call operations, resolve urgent production issues, mentor developers, and conduct code reviews.

Lead the architecture, development, and productionization of scalable data pipelines and governed data products in Databricks. Transform heterogeneous source data for analytics, AI, and machine learning; establish data quality, observability, governance, and delivery standards; and support experimentation, model development, deployment, and monitoring. Lead cross-team initiatives, influence technical roadmaps, mentor engineers and data scientists, and develop reusable AI and multi-agent data capabilities.

Found and establish GM Vehicle Autonomy’s centralized SRE practice. Build shared reliability tooling, service catalogs, SLOs, error budgets, readiness automation, standards, and production infrastructure. Partner with service owners to assess criticality, dependencies, failure modes, and operational health. Improve incident response, post-incident practices, on-call sustainability, and toil reduction while driving adoption across infrastructure teams. The role requires strong software engineering and distributed-systems expertise across Linux, networking, Kubernetes, cloud, CI/CD, and observability.