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FieldAI Tech Stack

Foundation models and autonomy stack for industrial robots in unstructured environments

Robotics Engineering Irvine, CA 201–500 employees Privately Held
fieldai.com LinkedIn

FieldAI builds autonomous control systems for industrial robots operating in complex, unstructured real-world settings. The tech stack spans robotics fundamentals (ROS, ROS 2, LiDAR, GPU acceleration via CUDA/PyTorch) paired with cloud infrastructure (AWS, SageMaker) and physics simulation (Isaac Sim, Gazebo, MuJoCo) — a foundation-model-first approach to robot autonomy. Engineering-heavy hiring (66 of 77 roles) focused on mid-to-senior levels signals active development across the full autonomy stack, while active projects on deployment playbooks, real-time data pipelines, and field-hardened testing suggest the company is moving from research prototypes toward production-grade fleet operations.

Tech Stack 79 technologies

Core StackPython C++ PyTorch TensorFlow AWS SageMaker Terraform CloudFormation Jira Wrike Confluence ROS USB I2C GPU LiDAR ARM x86 Ubuntu Yocto Jetpack ROS 2 Isaac Sim Gazebo CUDA MuJoCo PyBullet OpenCV ONNX TensorRT+49 more
AdoptingLiDAR

What FieldAI Is Building

◆Challenges

  • Tight hardware-software integration
  • System reliability
  • Harsh environment operations
  • Ensuring reliability in field environments
  • Transition research to production
  • Customer adoption acceleration
  • Improving software quality
  • Deploying robots in unstructured environments
  • Narrow passage navigation
  • System scalability

▲Active Projects

  • Field deployment of robotic systems
  • Build deployment playbooks
  • Field foundation models development
  • Field-insight foundation model (fifm) infrastructure
  • Implement monitoring tools
  • Autonomy layer control integration
  • Ros/ros2 packages for robotic autonomy
  • Real-time data processing pipelines
  • Testing pipeline development for algorithm evaluation
  • Control algorithm development for trajectory tracking

Hiring Activity

Accelerating75 roles · 65 in 30d

Department

Engineering
66
Research
2
Design
1
Finance
1
HR
1
Legal
1
Operations
1
Ops
1

Seniority

Mid
36
Senior
27
Intern
5
Staff
3
Lead
2
Director
1
Junior
1
Manager
1
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About FieldAI

FieldAI develops foundation models and autonomous control software for industrial robots deployed in unstructured, high-risk environments. The core product is the Field Foundation Model (FFM), a physics-aware system that learns robot behavior from live field deployments, continuously improving reliability without relying on pre-trained data, GPS, or pre-programmed routes. The company operates across the full robotics stack — from hardware integration and real-time perception (LiDAR, computer vision) through trajectory planning and control algorithms, to cloud-based fleet management and monitoring. With 201–500 employees based in Irvine, CA and hiring across the US, UK, Japan, Singapore, and Switzerland, FieldAI targets industrial operations where manual intervention is dangerous or infeasible.

HeadquartersIrvine, CA
Company Size201–500 employees
Hiring MarketsUnited States, United Kingdom, Japan, Singapore, Switzerland

Frequently Asked Questions

What tech stack does FieldAI use for robot autonomy?

Core autonomy runs on ROS/ROS 2 with Python and C++, leveraging LiDAR and GPU acceleration (CUDA, PyTorch, TensorRT). Simulation relies on Isaac Sim, Gazebo, and MuJoCo; cloud deployment uses AWS and SageMaker for training and fleet management.

What is FieldAI working on?

Active development includes the Field Foundation Model (FFM) and Field-Insight Foundation Model (FIFM) infrastructure, ROS/ROS 2 autonomy packages, real-time data processing pipelines, field deployment playbooks, and control algorithms for trajectory tracking in unstructured environments.

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How this profile is built

FieldAI'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.