Top Tech Jobs & Startup Jobs

Define and own networking architecture for next-generation super-pod ML hardware. Lead R&D, specify high-performance networking fabrics, drive ML networking roadmap, collaborate cross-functionally across silicon, systems, and software, contribute to industry standards, and deliver performant, reliable, and secure interconnect solutions at scale.

Create technical messaging, demos, collateral, and enablement for AI infrastructure and inference use cases. Translate hardware and architecture details into customer- and field-facing materials, support launches and benchmarking, and partner with product, engineering, sales, and field teams to refine positioning and technical proof points.

Design and define hyperscale AI compute network architectures. Translate customer and workload needs into scalable, cost‑optimized network and compute product requirements. Lead cross-functional integration, fabric design (InfiniBand/RoCE/Ethernet), lossless Ethernet tuning, high‑speed optics/cabling decisions, lifecycle operations, and deployment strategies for large AI clusters.

As a Senior AI Systems Performance Engineer, optimize and scale foundation models, enhance performance across various system layers, and deliver high-performance AI applications.

The Principal Engineer will lead post-silicon validation and debugging of high-speed IO and memory subsystems, ensuring performance and reliability for AI compute platforms.

Responsible for post-silicon validation, debug, and validation of high-speed IO and memory subsystems to ensure reliable AI systems. Lead efforts in debugging, product validation, and development of tools for automation.

The Process/Quality Engineer will enhance manufacturing processes, ensure product quality, and drive compliance while collaborating with cross-functional teams to support new product launches and continuous improvement initiatives.

Own and develop Cadence-based RTL-to-GDS physical design flows for RDU chips. Maintain and enhance Genus/Innovus/Cerebrus toolflows, script automation (TCL/Perl/Python), collaborate with physical designers and tool vendors, and ensure compliance with advanced TSMC node design rules (3nm and below).

As a Cloud Site Reliability Engineer, you will ensure the reliability, performance, and scalability of AI inferencing services, participate in on-call rotations, manage cloud infrastructure, and automate CI/CD processes while collaborating on incident management and capacity planning.

Embed with enterprise customers to design, build, and deploy production generative AI applications on SambaNova hardware and SambaStack. Architect LLM workflows (RAG, agents, fine-tuning), optimize inference performance, troubleshoot across model/software/hardware layers, translate field needs to product teams, support technical sales and POCs, develop reusable reference architectures, and present results to customers and internal stakeholders.