Teleo
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About
Teleo is a Palo Alto-based technology company that specializes in retrofitting heavy construction and mining equipment with autonomous and remote-control capabilities. Their flagship system, Teleo Supervised Autonomy, is designed to transform traditional machinery—such as dozers, loaders, and dump trucks—into semi-autonomous assets. The primary objective of this technology is to enhance job site safety and operational efficiency by allowing human operators to control machines from a distance. By blending advanced machine learning with the nuanced decision-making skills of human professionals, Teleo enables a hybrid approach to site automation where machines handle repetitive, low-complexity tasks while humans remain in the loop for more intricate maneuvers. The technology works through a hardware-and-software integration kit that is installed directly onto existing equipment. Once retrofitted, these machines can be operated from a specialized remote command station equipped with high-definition monitors and tactical joysticks. The system’s AI components manage tasks like autonomous hauling, which allows a single operator to supervise multiple machines simultaneously. This "supervised autonomy" model ensures that if a machine encounters a situation it cannot handle on its own, the remote operator can immediately take control. This setup significantly reduces the physical strain on operators and eliminates the need for them to be physically present in hazardous or isolated locations. Teleo is specifically designed for heavy industries such as construction, mining, and land development. It is an ideal solution for fleet managers who have idle equipment due to labor shortages or for companies operating in high-risk environments where operator safety is a primary concern. Because the system is brand-agnostic, it is particularly valuable for organizations with mixed-brand fleets, allowing them to modernize their current inventory without the massive capital expenditure of buying new autonomous machines. By shifting the operator's role from a physical cab to a secure, office-based environment, Teleo also helps companies attract a broader range of talent to the industry.
Pros & Cons
Supports mixed-brand fleets through retrofitting of existing heavy machinery.
Improves worker safety by moving operators from hazardous cabs to secure remote environments.
Allows a single operator to manage multiple machines, increasing site efficiency.
Enables companies to bring idle machinery back into service despite labor shortages.
Provides a safer, office-like environment that can help attract a broader workforce.
Requires physical installation of specialized hardware kits onto each machine.
Not a fully autonomous solution as it still requires human supervision for complex tasks.
Use Cases
Fleet managers can automate repetitive dirt hauling tasks using autonomous dump trucks while supervising from a central hub.
Construction companies can deploy remote-controlled loaders in dangerous terrain to protect human operators from physical injury.
Site owners facing labor shortages can utilize one skilled operator to manage several dozers simultaneously through the remote station.
Platform
Features
• ai-driven machine learning
• joystick and monitor command stations
• brand-agnostic hardware integration
• multi-machine supervision
• semi-autonomous hauling
• supervised autonomy system
• remote-control operation
• heavy equipment retrofitting
FAQs
What equipment is compatible with Teleo technology?
Teleo can be retrofitted onto various types of heavy construction equipment, including dozers, loaders, and dump trucks. The system is brand-agnostic, allowing fleet owners to upgrade their current mixed-brand assets.
How does the remote operation system work?
Operators run machines using a remote command station equipped with joysticks and monitors. This setup allows them to control equipment from virtually anywhere, moving them from the physical cab to a safe environment.
Is the machinery fully autonomous?
The system uses Supervised Autonomy to create semi-autonomous machines that handle repetitive tasks like hauling. A human operator remains in the loop to manage more complex maneuvers or intervene when necessary.
Can one person operate more than one machine?
Yes, Teleo's technology allows remote operators to supervise multiple machines simultaneously. This efficiency gain helps companies put idle equipment back into service and better manage labor shortages.
Pricing Plans
Custom
Unknown Price• Heavy equipment hardware retrofitting
• Supervised autonomy software
• Remote operator station setup
• Multi-machine control capability
• Brand-agnostic integration
• Operator training support
Job Opportunities
Robotics Technician, R&D Harness Manufacturing
Retrofit heavy construction equipment with supervised autonomy to enable remote operation, improving safety and efficiency for operators in hazardous environments.
Experience Requirements:
Extensive practical experience in manufacturing custom harnesses for automotive or similar applications.
Demonstrated expertise in sealed and un-sealed connectors, including but not limited to TE, Amphenol, Molex, Yazaki, Aptiv, Delphi, etc…
Hands-on proficiency in wire stripping, crimping, splicing, and harness dressing to a class 2 of IPC/WHMA-620 standard.
Practical experience with wiring tailored to various communication protocols such as CAN, Serial, LIN, RF, and Ethernet.
Ability to interpret electrical schematics generated from CAD tools like Altium, Cadence, Kicad, or similar software.
Other Requirements:
Skilled in using hand and power tools, as well as workshop equipment like drill presses, band saws, etc.
Proven experience with task tracking tools such as JIRA, Asana, Monday, or similar platforms.
Comfortable working with Linux OS and using Linux command lines.
Must be fluent in English, both spoken and written.
Willingness to travel domestically and internationally up to 10%.
Responsibilities:
Manufacturing, validating, and integrating 1st harness articles on next-gen vehicles.
Interacting daily with engineers.
Designing complex harnesses that meet automotive-quality standards.
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Senior Autonomy Controls Engineer – Learning-Based Control
Retrofit heavy construction equipment with supervised autonomy to enable remote operation, improving safety and efficiency for operators in hazardous environments.
Experience Requirements:
Strong software engineering skills in C, C++, or Python (production-quality code)
Deep understanding of modern robotics control systems
Experience with learning-based control or policy optimization for real-world systems
Comfort working close to hardware and real-time constraints
Responsibilities:
Design and implement learning-based control approaches (imitation learning, reinforcement learning, hybrid MPC + learning)
Reduce dependence on hand-tuned control parameters through data-driven methods
Integrate learned controllers into the existing vehicle control stack safely and incrementally
Define interfaces between classical control (MPC, PID, state estimation) and learning-based components
Establish validation criteria for learned control policies before real-vehicle deployment
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Senior Full Stack Engineer
Retrofit heavy construction equipment with supervised autonomy to enable remote operation, improving safety and efficiency for operators in hazardous environments.
Experience Requirements:
4+ years of professional full-stack development (Python + React/TypeScript)
Strong backend fundamentals: REST API design, authentication, database modeling
Production AWS experience (Lambda, EC2, S3, DynamoDB, or comparable services)
DevOps experience: CI/CD pipelines, deployment automation, and production operations
Demonstrated ability to own and ship products end-to-end with minimal oversight
Other Requirements:
Comfortable working in office in Palo Alto, CA
Responsibilities:
Own the architecture and development of customer-facing dashboards and internal tools
Consolidate and harden a landscape of full-stack applications into maintainable, well-tested production services
Define and enforce engineering standards: API design, code quality, testing, CI/CD, and deployment practices
Build and maintain backend services in Python and Node.js on AWS
Develop responsive frontend experiences in React with TypeScript
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