ASIC Network Architect
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Role details
Tech stack
Job description
SambaNova Systems is looking for a talented and motivated ASIC Network Architect to help us solve the most challenging and impactful problems in machine learning and artificial intelligence., * Conduct research and development in state of the art networking solutions
- Define and own networking architecture specifications for performant, robust, and efficient communications on the next generation super-pod scale ML hardware
- Define and own the ML networking roadmap, leading a cross-functional effort across software, systems, and silicon
- Work with executive leadership to align network initiatives with product competitiveness requirements
- Track and contribute to industry standards, representing SambaNova
Requirements
- B.S. Computer Science, Computer Engineering or Electrical Engineering
- 10+ years of industry experience in ML silicon development in GPU, ML accelerator or networking ASIC
- Proven track record of successful full-cycle development and delivery from planning to product in networking for such silicon solutions
- Track record of ownership and definition of various networking and routing protocols
- Deep understanding and hands-on experience defining various networking topologies for large machine clusters.
- Deep understanding of congestion management, QoS, security isolation
- Deep understanding of networking reliability, availability and serviceability
- Capable of abstracting and analyzing network performance from real world application data
- Capable of breaking down networking requirements into silicon, packaging, system SW, RunTime and Compiler SW requirements
- Strong hands-on analytical and debug skills to make progress on challenging problems in an ambiguous and highly dynamic environment
- Strong coding skills with C++, Python, or Rust
- Highly motivated, self-driven, independent, organized and strong collaboration and communication skills
- Cross-functional team leadership experience, * M.S. or PHD degree in Computer Science, Computer Engineering or Electrical Engineering
- 15+ years of industry experience in ML / supercomputer / public cloud / networking industries
- Good understanding of ASIC development cycles
- Knowledge of machine learning architecture and/or computer architecture
- Experience in building or proficiently using network simulators
- Experience in leading silicon, packaging, system HW, system SW cross-functional teams
- Experience in conducting perf and perf/tco trade-off analysis on different interconnect choices
- Familiarity with off the shelf optical IP and optical switches
- Understanding / hands-on experience in end to end optical systems
- Hands-on experience in working with IP/Switch hardware vendor to enhance / customize solutions
Benefits & conditions
SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD&D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.
About the company
SambaNova is a leader in next-generation AI infrastructure, delivering a full-stack inference platform for customers worldwide. At the core of SambaNova’s technology is the RDU (Reconfigurable Dataflow Unit) - a chip built on a dataflow architecture rather than the traditional GPU model. Its decode performance is especially strong for agentic workloads like multi-turn agents, code generation, and long-running applications. RDUs are packaged into SambaRack, rack-scale hardware that lets customers deploy state-of-the-art models with better performance, greater energy efficiency, and faster time to value.
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