Top Tech Jobs & Startup Jobs in Austin, TX

Design, curate, and evaluate large-scale text datasets for LLM pretraining. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; and collaborate with Safety, Ethics, and Governance to ensure dataset quality, privacy, and responsible AI alignment.

Lead a team of SREs to operate large-scale hybrid CPU/GPU AI training and inference infrastructure. Design observability, automation, deployments, incident management, security/compliance, and collaborate with ML teams to accelerate research-to-production workflows.

Lead end-to-end data science and AI engagements: design, develop, deploy and evaluate models, mentor teams, partner with stakeholders, drive adoption, and apply Responsible AI to deliver measurable business impact.

Lead infrastructure analytics for compute, storage, and networking: design scalable data pipelines, define core metrics, build dashboards and APIs, ensure data quality and governance, drive instrumentation fixes, and advise executives on capacity, utilization, and readiness.

Operate and improve large-scale HPC GPU clusters for AI model training and inference. Build observability, automation, CI/CD, and incident response tooling; lead on-call rotations, ensure security/compliance, collaborate with ML engineers, and optimize capacity and costs for production ML workloads.

Lead product efforts for Azure HPC & AI supercomputers by engaging customers, prioritizing backlog from telemetry and incidents, partnering with SREs and engineers to improve service health, build deployment strategies, and deliver AI-driven product improvements with security-by-default.

Design, deploy, operate, and tune large-scale HPC environments for AI model training. Own HPC domains (GPU compute, storage, networking), manage schedulers (SLURM, Kubernetes), build automation in Bash/Python, troubleshoot cluster performance, and partner with researchers to support high-scale training workloads.

Lead hardware-software co-design for datacenter-scale AI systems, analyzing real workloads to drive architecture, performance, and cost improvements; prototype and productionize optimizations across accelerators, runtimes, compilers, interconnects, and distributed training/inference; mentor engineers and influence hardware and platform roadmaps.

Design, develop, and operate monitoring and observability solutions for flagship supercomputers. Build data pipelines to process large telemetry and logs, respond to incidents, implement mitigations, write postmortems, and improve reliability, performance, and operational runbooks at scale.

Join Azure PostgreSQL to design, implement, and test features in a relational database engine (query processing, logging, storage, IO, scale-out). Collaborate with stakeholders, write maintainable code, define test plans, operate live services (on-call), and contribute to open-source PostgreSQL and Linux.