Top Artificial Intelligence Jobs

Lead Casa’s Austin home-services operation, owning service delivery across handymen, concierge teams, and vendor partners. Build dashboards and operating systems to improve completion, quality, cost, productivity, and homeowner satisfaction. Recruit and manage third-party vendors and frontline teams, resolve service failures, improve processes, and uphold home-access, safety, and trust standards. The role requires hands-on field involvement, independent project ownership, stakeholder management, and flexible availability for customer needs.

Own the enterprise architecture vision for scalable, high-performance, cost-efficient multi-tenant SaaS systems. Lead AWS cloud optimization, distributed data and AI infrastructure, architectural governance, and Architecture Review Council activities. Guide engineering teams on microservices, event-driven systems, automation, and cloud-native practices while driving GenAI adoption across the software lifecycle. Build prototypes, establish architectural standards, optimize total cost of ownership, and mentor technical leaders.

Own end-to-end technical engagements involving AI evaluations, embedded assessments, and rapid-response incident analysis. Scope projects, coordinate technical teams, manage dependencies, guide methodological decisions, maintain partner relationships, interpret evaluation results, and present findings to technical, governmental, media, and public audiences. The role also develops repeatable workflows and playbooks while collaborating with research and governance teams.

Own technical support for enterprise SaaS customers by triaging and resolving complex issues, especially SSO and SCIM problems. Investigate logs, APIs, and browser tools; communicate status to Customer Success and Engineering; and manage escalations through resolution. Build runbooks, decision trees, and automation using AI tools to improve support scalability and enable self-service. Participate in customer troubleshooting calls and continuously improve support processes.

Researches, develops, tests, and maintains materials-aware machine learning models and software. Designs scalable ML systems, improves model interpretability and uncertainty quantification, and develops inverse-design capabilities. Collaborates with product, engineering, and external research teams to deliver customer-focused AI solutions, publishes research, reviews code, analyzes model performance, and mentors developers.

Design, build, and scale backend services, APIs, and workflow systems supporting healthcare claims identification and recovery. Integrate AI/ML models, legal datasets, and transactional data while developing fault-tolerant Temporal workflows. Maintain AWS infrastructure and secure deployment pipelines using Python, TypeScript, PostgreSQL, Redis, and related technologies. Collaborate with product, business, and data science teams to deliver reliable, secure, high-performance systems in a regulated startup environment.

Own strategy and revenue for 5–7 strategic utility accounts, originating and closing complex enterprise software deals. Lead discovery, solution shaping, business-case development, procurement, negotiation, contracting, and expansion. Coordinate cross-functional pursuit teams, manage executive and technical stakeholders, maintain forecasts and pipeline discipline, and translate market feedback into product and go-to-market strategy. The role requires deep enterprise sales expertise, utility or regulated-industry familiarity, and the ability to sell technically complex AI and data platforms.

Own a product domain end-to-end, from discovery and strategy through launch, adoption, and iteration. Define roadmaps and requirements, collaborate with engineering and design, and deliver features across frontend, backend, and AI/ML systems. Use data, user research, and judgment to prioritize work, align stakeholders, maintain product quality, and drive rapid experimentation in a resource-constrained startup environment.

Design and develop rugged hardware and PCB solutions for industrial and heavy-duty connected devices. Integrate microcontrollers, embedded systems, CANBus, SAE J1939, wireless technologies, and device-to-cloud architectures. Create prototypes, specifications, DFMEA documentation, validation plans, and technical documentation while optimizing designs for manufacturing and system integration. Collaborate with firmware, mechanical, thermal, and optical engineering teams throughout the product development lifecycle.

Lead platform engineering across infrastructure, CI/CD, testing, developer tooling, observability, and secure software delivery. Build internal platforms, golden paths, and shared tooling that improve developer and AI-agent productivity. Establish engineering standards, integrate security controls into pipelines, shape repositories for coding agents, and measure impact through DORA metrics and engineering feedback.