Jobs at SambaNova Systems

Design and implement runtime-stack features for scalable, high-performance ML inference and training. Develop system software, drivers, kernels, user-space libraries, management tools, profiling and debugging capabilities. Optimize hardware utilization and performance across distributed systems, collaborate with hardware, compiler, ML application, and DevOps teams, and build, test, and deploy software for AI and HPC platforms.

Lead SambaNova’s EMEIA sales organization and regional go-to-market strategy across Europe, the Middle East, India, and Africa. Build and coach sub-regional leadership and sales teams, execute product launches, establish repeatable sales methodologies, improve forecasting and pipeline discipline, support strategic enterprise deals, and develop C-level relationships. Partner cross-functionally with operations, marketing, product, customer success, and partnerships teams to drive predictable revenue growth across enterprise, government, neo-cloud, and model-maker segments.

Own full lifecycle recruiting for semiconductor hardware engineering roles, including design verification, ASIC/RTL design, physical design, and PCB/systems engineering. Partner with hiring managers to define roles, build pipelines, design interviews, source passive silicon talent, coordinate feedback, close candidates, and maintain recruiting data and metrics in Greenhouse.

Drive planning and execution of multiple software and ML workstreams, manage cross-functional dependencies, gather requirements, track schedules, support development-to-deployment, and improve processes to deliver customer product releases.

Develop and enhance runtime stack features for high performance ML applications, manage system software, and collaborate with teams to optimize AI systems.

Architect, build, and operate an internal developer platform supporting AI workloads across AWS, GCP, Azure, Kubernetes, and on-premises environments. Responsibilities include Terraform-based infrastructure automation, highly available Kubernetes clusters, hybrid-cloud networking, security, observability, capacity planning, deployment, incident response, and on-call support. The role partners with AI and software engineering teams to provide production-ready tools and workflows.

As a Software Architect, you will define the SambaStack platform's technical direction, ensure system scalability, and resolve architectural challenges while mentoring engineers.

Lead development and optimization of the compiler stack for ML systems, collaborate across teams, integrate and deploy products, map ML operations to hardware, and drive compiler infrastructure innovation and performance debugging.

The Supply Chain Specialist manages inventory, tracks supply demand, issues POs, and collaborates with manufacturing partners to support operations and ensure product delivery.

The role involves leading a software engineering team focused on AI infrastructure and enterprise software, ensuring technical quality and team development while also contributing as a hands-on engineer.

Build and improve software for SambaRack, a rack-scale AI infrastructure platform. Responsibilities include developing hardware management, monitoring, control, diagnostics, telemetry, observability, and infrastructure services; improving performance, reliability, and scalability; integrating software with hardware; resolving bottlenecks; and collaborating with hardware, DevOps, QA, and product teams. The role supports production, enterprise, customer-managed, and air-gapped deployments.

Design and build reliable software for SambaRack, a rack-scale AI infrastructure platform. Responsibilities include hardware control, monitoring, diagnostics, telemetry, observability, distributed systems, and hardware-software integration. The role improves system performance, scalability, and reliability; leads technical architecture; resolves systemic risks; collaborates across engineering teams; and mentors engineers supporting enterprise and air-gapped deployments.

Define and own the end-to-end architecture of SambaStack, an enterprise AI inference platform. Set technical strategy and standards across engineering teams, lead design reviews, resolve scalability and reliability risks, and guide platform evolution. Partner with ML, product, QA, and DevOps teams to make architectural decisions, evaluate emerging technologies, mentor engineers, and design systems for production-scale AI inference workloads.

Design and own the architecture of SambaRack’s infrastructure and management platform for rack-scale AI systems. Lead hardware-software integration, control, monitoring, diagnostics, observability, reliability, and scalability efforts. Establish architectural standards, guide technical direction, resolve systemic risks, and collaborate with Hardware Engineering, DevOps, QA, and Product teams. Mentor engineers and evaluate technologies for enterprise, production, customer-managed, and air-gapped deployments.

Lead development of SambaNova’s enterprise AI inference serving platform. Responsibilities include improving performance, scalability, reliability, and platform capabilities; designing backend components for AI inference; building testing frameworks and automation; solving distributed-systems challenges; and driving technical execution and software quality across cross-functional engineering initiatives.

Own and operate a global AI inferencing platform: ensure uptime, low-latency performance, scalability and cost efficiency. Build monitoring, alerting, CI/CD, IaC, autoscaling, capacity planning, incident response, and participate in a shared on-call rotation to maintain 24/7 service reliability.

Designs end-to-end data center reference architectures integrating SambaNova hardware and networking. Supports customer deployments, business development, demonstrations, proofs of concept, technical project management, troubleshooting, training, and product feedback. Requires deep networking expertise, system-level knowledge spanning Linux, kernel drivers, PCIe, NICs, and hardware architecture, plus customer-facing presentation skills. Travel is required approximately 30% of the time.

Design and implement RTL components for AI accelerator ASICs from microarchitecture through tape-out and silicon bring-up. Responsibilities include writing and debugging SystemVerilog, optimizing power, performance, and area, supporting synthesis and timing closure, and applying lint, CDC, LEC, and assertion-based quality checks. The engineer collaborates with architecture, verification, and physical design teams while contributing to compute datapath, on-chip network, and memory subsystems.

Own microarchitecture and synthesizable SystemVerilog RTL for major AI accelerator blocks from specification through tape-out. Drive power, performance, and area optimization, timing closure, synthesis, STA, lint, CDC, LEC, assertions, and verification collaboration. Partner with architecture and software teams on performance improvements, advance EDA automation, lead design reviews, and mentor engineers.

Leads end-to-end design verification for next-generation machine learning ASICs through tape-out and first-silicon bring-up. Owns verification architecture, methodology, coverage, performance validation, emulation, FPGA prototyping, debug, schedules, and tape-out readiness. Manages and grows an experienced team of 8–10 engineers, including principal-level staff, while partnering with architecture, design, software, compiler, runtime, hardware, and systems teams.