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System Software Engineer, Node & Cluster Management

MatX · Mountain View

CHECKED 9H AGO

Infrastructureremotesenior
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At a glance

Highlights

  • Build node-level management plane exposing health, telemetry and control operations.
  • Extend node management to cluster scale with failover and fleet health aggregation.
  • Unify in-band and out-of-band management into one coherent operator view.

What they offer

  • Friday food ordering via WeBox, Specialty's or reimbursement system
  • 100% paid daily commute via company Uber account or train reimbursement
  • $50/mo MatX Extras perk allowance
  • $35/mo cellular and $40/mo wifi reimbursement
  • Up to $1000 in home office setup via Amazon business account
  • 100% paid mental health benefit via SpringHealth and Guardian EAP
  • Up to 12 weeks paid parental leave, 10 weeks pregnancy disability leave, flexible return-to-work and Benepass reproductive health benefit

Ideal candidate

Self-driven systems engineer with deep low-level Linux instincts who takes hardware specs and starts building, loves debugging across hardware to API, and wants to define manageable-at-scale AI infrastructure.

Requirements

  • BS or higher in Computer Science, Electrical Engineering, or equivalent practical experience with 8+ years in systems software
  • Strong hands-on Linux systems development experience, including low-level userspace software
  • Comfortable reading and debugging kernel driver and daemon code
  • Strong programming skills in C plus a systems language such as Go, Rust, C++ and/or Python
  • Experience designing and building HTTP/REST APIs and CLI tools for hardware or infrastructure management
  • Solid understanding of device drivers, telemetry paths, PCIe device behavior and BMC-managed subsystems
  • Experienced debugging across API, daemon, kernel, firmware and hardware boundaries

Lab profile

About MatX

Designs vertically integrated AI silicon and systems to run frontier models at physics-limit efficiency.

What they build

  • AI accelerator silicon for GenAI training and inference
  • Rack-scale AI data center systems
  • Full-stack silicon-to-software platform for large ML workloads

Known for

  • Vertically integrated silicon-to-systems design
  • AI accelerator architecture and performance modeling for LLMs
  • High-efficiency power architecture from chip to rack
  • Silicon verification and software-hardware co-verification
More about MatX →
Apply on the lab’s site ↗