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Foundation Tech Stack

General-purpose humanoid robotics with custom silicon and learned control

Robotics Engineering San Francisco, California 51–200 employees Founded 2024 Privately Held

Foundation is building hardware and software for general-purpose humanoid robots, founded in 2024 and now scaling fast across 51–200 employees. The stack reveals a hardware-first, in-house capability strategy: custom PCB design (Altium, KiCad), mixed-signal electronics (Cadence, ANSYS), FPGA/ASIC work (Verilog, SystemVerilog), real-time embedded (ARM, RTOS, VxWorks, FreeRTOS), and ML inference (PyTorch, JAX, TensorFlow, ORB-SLAM3). The hiring velocity—29 engineering roles out of 38 open, weighted toward senior and lead levels—tracks a concurrent factory ramp and a push to eliminate vendor reliance through custom inference silicon and PCB controllers.

Tech Stack 69 technologies

Core StackJava C++ Python PyTorch TensorFlow Embedded C C/C++ ARM RTOS EtherCAT VxWorks FreeRTOS Yocto ROS 2 Verilog VHDL SystemVerilog ASIC FPGA Altium Designer KiCad Cadence ANSYS COMSOL MuJoCo JAX ROS Isaac Sim ORB-SLAM3 Cartographer+38 more
AdoptingCUDA BMS

What Foundation Is Building

Challenges

  • Cost efficiencies
  • Aggressive ramp targets
  • Labor shortage
  • Cut vendor reliance
  • Bottlenecks in robotic inference pipelines
  • Seamless integration of hardware and software
  • Scalable manufacturing
  • Cold and utilitarian designs
  • Reducing vendor dependencies
  • Accelerating prototyping

Active Projects

  • Custom electronics design for robotics
  • Factory launch
  • Sensor integration for robotic subsystems
  • Custom inference chip evaluation
  • Embedded systems and mixed-signal circuits design
  • Hardware-in-the-loop validation of learned policies on real-world robot platforms
  • Development of in-house rl training pipelines and tooling
  • Real-time control and locomotion of humanoid robots
  • Data logging infrastructure
  • Custom pcbs for robotic hand controllers

Hiring Activity

Accelerating40 roles · 15 in 30d

Department

Engineering
29
Manufacturing
4
HR
3
Design
1
Executive
1

Seniority

Senior
17
Mid
6
Lead
4
Staff
4
Intern
3
C-Level
2
Junior
1
Principal
1

Notable leadership hires: Motor Actuator Lead, Chief of Staff

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About Foundation

Foundation develops general-purpose humanoid robots designed to operate in high-risk and labor-intensive environments. The company's charter is to address structural labor shortages in industries like conflict response and manufacturing by building robots capable of complex, real-world task execution. Engineering is the core of the operation: the team spans embedded systems, hardware design, ML/RL, sensor integration, and factory production. Current priorities include real-time locomotion and control, in-house RL training pipelines, custom inference chip evaluation, and manufacturing scale-up. The organization is headquartered in San Francisco and hiring exclusively in the United States.

HeadquartersSan Francisco, California
Company Size51–200 employees
Founded2024
Hiring MarketsUnited States

Frequently Asked Questions

What technologies does Foundation use to build robots?

Foundation uses embedded C, C++, ARM, RTOS (FreeRTOS, VxWorks), ROS 2, PyTorch, JAX, TensorFlow, ORB-SLAM3, FPGA/ASIC design (Verilog, SystemVerilog), and real-time control stacks. They are actively evaluating CUDA and custom inference chips.

What is Foundation working on right now?

Current projects include custom electronics and PCBs for robotic controllers, factory launch, sensor integration, custom inference chip evaluation, in-house RL training pipelines, real-time humanoid locomotion control, and hardware-in-the-loop validation on real robot platforms.

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How this profile is built

Foundation's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →

This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.