Top Tech Jobs & Startup Jobs in Austin, TX

Contribute to building and scaling ML development infrastructure for Copilot: design and optimize CI/CD for large-scale training, build developer tools, maintain multi-cloud infrastructure, and deploy/host models for inference and data generation while collaborating with cross-functional teams to improve reliability, performance, and model development workflows.

Design and build compute orchestration and scheduling on Kubernetes and Ray for large-scale AI model training and inference. Tune and benchmark pretraining software on Nvidia GB200 and AMD MIxxx GPU clusters, improve cluster efficiency, enable observability and reliability, collaborate with research and framework teams, and influence architecture and roadmap for Copilot and foundational models.

Develop and iterate post-training methods for large language models: drive data collection, build evaluations, design experiments, and apply reward modeling and RL techniques to improve model capabilities and deployment.

Design, curate, and evaluate high-quality multimodal datasets for foundation models. Build scalable ingestion, preprocessing, filtering, annotation pipelines; run data ablations and analyses; create auditing, visualization, and versioning tools; collaborate with Safety, Ethics, and research teams to ensure data quality, privacy, and responsible AI alignment.

Design and develop multimodal foundation-model algorithms and architectures, run large-scale experiments, improve training and deployment efficiency, build datasets and pipelines, perform rigorous evaluation and ablation studies, and collaborate with infrastructure, data, and product teams to responsibly advance AI systems.

Drive product strategy and KPIs for backend networking of ultra-large AI supercomputers. Identify failure points, lead remediations and roadmap changes, and coordinate cross-functional teams including customers, datacenter operators, vendors, and product groups to maximize uptime and AI workload throughput.

Design, implement, and optimize low-level software for Azure HPC/AI VMs. Diagnose HW/SW integration and performance issues, drive cross-team solutions, maintain service stability, and develop operational playbooks.

Design and implement next-generation networking infrastructure for large-scale AI training on Azure. Lead architecture, write secure high-performance code, create test strategies, perform and coach code reviews, integrate AI tools in the SDLC, and collaborate cross-team to ensure scalability, reliability, observability, and security for distributed HPC/AI systems.

Design, build, and optimize high-performance, low-latency networking infrastructure for large-scale AI training on Azure. Benchmark and improve scalability, reliability, and observability; debug complex network issues; author design documents; act as DRI and participate in on-call operations to restore and maintain service availability.

Design and build monitoring, telemetry, and data pipelines to operate flagship supercomputers at scale. Diagnose and troubleshoot GPU hardware, networking, datacenter and software stack issues, implement systemic mitigations, improve observability, and drive reliability and performance improvements through incident reviews and tooling.