Top Tech Jobs & Startup Jobs in Austin, TX

Own roadmap and product direction for ontology and data products. Translate messy operational domains into data models and specs. Ship end-to-end with engineers, validate with stakeholders, and define metrics proving operational leverage.

Lead end-to-end R&D program plans for hardware and infrastructure: scope, sequencing, owners, and critical path from concept through prototype, qualification, and production handoff. Run NPI programs, coordinate design reviews, procurement, lab/test capacity, surface risks, drive cross-functional execution with manufacturing, supply chain, and deployment, and build leadership reporting while optimizing for velocity.

Own and scale the GPU compute fleet: build metrics, alerting, observability, and repair automation; design GPU qualification/burn-in pipelines; own Redfish/BMC tooling and firmware telemetry; run incidents, eliminate toil, and deliver end-to-end reliability and orchestration for Kubernetes and bare-metal compute at hyperscale.

Build and operate large-scale GPU compute fleet tooling: metrics pipelines, alerting, Redfish/BMC telemetry, repair/RMA automation, GPU qualification, and orchestration for Kubernetes and bare-metal deployments. Own reliability, scalability, incident response, and automation to minimize manual toil.

Model and track fleet capacity (power, space, cooling, compute), forecast constraints, connect deployment schedules to sellable capacity, run scenario analyses to inform allocation and deal decisions, and build executive-ready reporting.

Build AI-powered internal tools and shared AI infrastructure (agents and skills), integrate LLMs with internal systems, design and ship web applications, and enable cross-functional adoption through documentation and support.

Build and operate GPU fleet infrastructure: metrics, alerting, Redfish/BMC tooling, repair and qualification pipelines, automation for failure detection through RMA, and ensure reliability, scalability, and observability for Kubernetes-orchestrated and bare-metal GPU deployments.

Lead technical execution of large-scale network deployments: define ZTP and validation playbooks, set acceptance criteria, resolve escalations across links and fabrics, and automate turn-up processes to eliminate manual repetition.

Lead product direction for ontology and data products that model and support compute production. Translate messy operational domains into data models, specs, and product surfaces; drive technical tradeoffs and ship end-to-end; run stakeholder discovery and validate impact; define and track success metrics to optimize product outcomes.

Design and develop the mechanical aspects of data-center robotics including end effectors, docking, and robot-plant interfaces. Rapidly prototype, test to failure, instrument trials, and productionize hardware with DFM, reliability, and spare strategies while collaborating closely with controls and software teams.