System Software Engineer, Node & Cluster Management
MatX · Mountain View
CHECKED 9H AGO
Infrastructureremotesenior
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