Thrive Capital Portfolio Careers

Thrive Capital
companies
Jobs

Software Engineer - Systems

Specter

Specter

Software Engineering
San Francisco, CA, USA
Posted on Nov 25, 2025

Location

San Francisco

Employment Type

Full time

Location Type

On-site

Department

Engineering

Company Background:

Specter is creating a software-defined “control plane” for the physical world. We are starting with protecting American businesses by granting them ubiquitous perception over their physical assets.

To do so, we are creating a connected hardware-software ecosystem on top of multi-modal wireless mesh sensing technology. This allows us to drive down the cost and time of deploying sensors by 10x. Our platform will ultimately become the perception engine for a company’s physical footprint, enabling real-time perimeter visibility, autonomous operations management, and “digital twinning” of physical processes.

Our co-founders Xerxes and Philip are passionate about empowering our partners in the fast approaching world of physical AI and robotics. We are a small, fast growing team who hail from Anduril, Tesla, Uber, and the U.S. Special Forces.

Role + Responsibilities:

Specter is hiring a systems software engineer to own distributed on-device software engineering for Specter’s mother node base stations.

Responsibilities include:

  • Building low-latency networking infrastructure connecting embedded and cloud systems

  • Building resource-efficient pipelines to egress/ingress multimodal sensor data and telemetry

  • Building low-latency command and control infrastructure across distributed sensor network

Qualifications:

  • 5+ years of experience in Rust (preferred) or C++ in low-latency and/or embedded settings

  • Deep experience working directly with networking protocols such as UDP, TCP, QUIC, etc.

  • Deep knowledge and understanding of IPC fundamentals and mechanics on Linux systems

  • Deep experience working directly with performant modern databases, distributed or otherwise

Preferences:

  • Experience working alongside real-time, multi-modal machine learning data ingestion systems

  • Experience with modern video codec implementations across various hardware platforms