Top Tech Jobs & Startup Jobs in Austin, TX

Manage and lead a software engineering team building NVIDIA DGX Cloud Kubernetes platforms. Responsibilities include gathering requirements, guiding engineers, developing cloud-based solutions, presenting roadmaps and demos, participating in open-source communities, and partnering across NVIDIA organizations. The role requires expertise in distributed systems, systems programming, Kubernetes, Linux internals, networking, storage, DevOps tooling, container orchestration, APIs, and identity and access management.

Leads a distributed team securing NVIDIA DGX Cloud’s large-scale GPU infrastructure. Responsibilities include hiring and developing engineers, setting strategy for security control planes and foundational services, delivering measurable risk reduction, maintaining engineering standards, communicating security risks to executives, and collaborating with platform, SRE, networking, and security teams. The role requires strong engineering management, infrastructure or security expertise, cloud-native fluency, and influence across partner organizations.

Productize NVIDIA GPU boards for datacenter server platforms, developing diagnostic tests, manufacturing screens, and debug procedures. Collaborate across engineering teams and contract manufacturers to define assembly lines, test stations, fixtures, and production processes. Analyze volume data, optimize yield and test time, ensure signal integrity and DFx compliance, and support quality teams in resolving customer and partner issues.

Designs and deploys large-scale AI and HPC GPU cloud infrastructure for NVIDIA Cloud Partners. Serves as the primary technical advisor across solution design, development, integration, production, debugging, and customer adoption. Builds proof-of-concepts, integrates AI software stacks, libraries, frameworks, and models, and delivers technical presentations and workshops. Partners with engineering, product, sales, and business teams to secure opportunities, guide product strategy, and support complex customer engagements throughout the lifecycle.

Designs and deploys large-scale NVIDIA AI and HPC GPU infrastructure for cloud partners and strategic customers. Leads technical engagements across the customer lifecycle, supports infrastructure development and production, builds networking and compute proofs of concept, troubleshoots performance and scaling issues, and delivers technical presentations and workshops. Partners with sales and business teams to identify opportunities and provides customer feedback to influence product strategy.

Design, integrate, and deploy large-scale AI/HPC GPU infrastructure with NVIDIA Cloud Partners. Drive technical engagements across the customer lifecycle, build PoCs, troubleshoot performance and scaling, and deliver technical content and workshops.

Develop and deploy deep learning models and petabyte-scale multimodal data curation pipelines for foundation-model training. Responsibilities include document extraction, OCR, layout and table analysis, deduplication, distributed processing, dataset and metric development, experimentation, production scaling, and deployment through NVIDIA Inference Microservices. The role also involves publishing research, creating technical documentation, communicating findings, and mentoring team members.

Design, develop, optimize, and debug ultra low-latency C++ streaming systems and media pipelines (WebRTC, GStreamer, RTP). Analyze performance across CPU, memory, networking, and scheduling. Integrate streaming software with platform services across Windows, Linux, QNX, and embedded platforms. Define KPIs for networking and streaming quality and troubleshoot across software, OS, platform, and hardware boundaries.

Lead HBM and LPDDR system integration and bringup for next-generation SoCs. Own memory performance, power, thermal, and reliability closure across silicon, package, board, and firmware. Drive post-silicon margining, VF shmoo, eye characterization, and correlation. Debug training, calibration, SI/PI, and system stability issues. Define validation, characterization, and issue-tracking plans to produce production-ready operating points.

The role involves owning physics models, defining manufacturing specifications, and validating designs to ensure product quality and performance. The architect will utilize first-principles thinking and AI to improve workflows and drive alignment across engineering teams.