S

Skild AI

General-purpose robotic AI brain via API

Full Stack Engineer - Frontend & Android Focus

Full-TimeUpdated on 10/23/2026
No salary listed
Mid
Bachelor's
San Mateo, CA, USA
In Person
Software EngineeringFull-Stack Engineering

About the job

Requirements
  • A bachelor's degree in Computer Science or a related field is required.
  • Strong proficiency in JavaScript and TypeScript is required.
  • Experience with React and modern frontend development is required.
  • Understanding of backend development principles is required.
  • Knowledge of database design and management is required.
  • Experience with containerization using Docker is required.
  • Familiarity with continuous integration and continuous delivery principles is required.
  • A basic understanding of cloud services is required.
Responsibilities
  • Design and develop responsive, high-performance web and Android applications for monitoring and controlling robotic fleets.
  • Implement browser and mobile visualization tools that render large-scale maps, point clouds, and 3D environments to visualize robot perception in real time.
  • Engineer robust data synchronization layers for network-denied environments or intermittent connectivity, maintaining seamless operation and data integrity when robots go offline.
  • Develop across the stack, emphasizing TypeScript and JavaScript for frontend logic and integration with backend services and robotic APIs.
  • Profile and optimize rendering pipelines and application logic to ensure smooth performance on varied hardware, from high-end workstations to mobile field devices.
  • Work with robotics researchers, machine learning engineers, and infrastructure teams to define APIs and data formats for real-time visualization.
Desired Qualifications
  • A proven track record of building and shipping complex, user-facing products at scale.
  • Deep proficiency in modern JavaScript ecosystems, including React, Vue, or similar frameworks, and state management.
  • Experience building native or high-performance cross-platform Android applications.
  • Experience with WebGL, Pixi, Three.js, OpenGL, or game engines such as Unity or Unreal, including rendering 3D geometry and high-frame-rate visualizations in browser or mobile contexts.
  • Experience rendering complex indoor environments and floor plans, and familiarity with spatial data structures and libraries suited to indoor navigation and visualization rather than traditional outdoor geospatial mapping.
  • Demonstrated ability to build systems that function reliably in network-constrained environments, including CRDTs, local-first databases, and optimistic UI updates.
  • Ability to understand the full flow of data from a robot sensor to a user's screen.

About the company

Skild AI creates a general-purpose artificial intelligence brain for robots. It provides a prefabricated AI model that can be integrated into various general-purpose robots, delivered via an API, to enable high-level decision-making without building custom software from scratch. The product works as a ready-made AI brain embedded in a robotic platform (starting with a mobile manipulator) and exposed through an API so developers and manufacturers can add human-like planning and control capabilities to their robots. This solution differentiates itself by offering a reusable, domain-agnostic AI core for multiple robotics systems rather than bespoke software for each robot, helping to lower development costs and accelerate deployment. Skild AI’s goal is to make robotics development more accessible by supplying the AI brain as a core component to robotics manufacturers and developers who want to build advanced robotic applications quickly and at scale.

Company Size

51-200

Company Stage

Series C

Total Funding

$1.8B

Headquarters

Pittsburgh, Pennsylvania

Founded

2023

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Simplify's Take

What believers are saying

  • Pittsburgh International Airport starts an xBridge pilot with Skild robots on October 1, 2026.
  • Skild disclosed a $100 million annual revenue run rate on September 10, 2026.
  • NVIDIA, Foxconn, and Sumitomo partnerships expand Skild into factories, kitchens, and semiconductor assembly.

What critics are saying

  • S1 still lacks broad independent validation, so real-world failures can stall enterprise rollouts.
  • Europe’s Machinery Regulation starts January 20, 2027, scrutinizing self-evolving robot behavior.
  • Trademark litigation with Idea Crossing ended July 7, 2026, but brand and IP disputes remain.

What makes Skild AI unique

  • Skild sells a hardware-agnostic robot brain, not a single-purpose robot, scaling across morphologies.
  • S1 learns new tasks from one video, eliminating retraining for each deployment.
  • The Zebra robotics acquisition added enterprise robotics integration and installed customer relationships.

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Benefits

  • Health Insurance
  • Dental Insurance
  • Vision Insurance
  • Unlimited Paid Time Off
  • Flexible Work Hours
  • Remote Work Options
  • Paid Vacation
  • Paid Sick Leave
  • Paid Holidays
  • Hybrid Work Options
  • Wellness Program
  • Mental Health Support

Growth & Insights and Company News

6 month growth

-3%

1 year growth

-18%

2 year growth

-10%
TechCrunch
Oct 23rd, 2026
Skild AI hits $100M in annual revenue as its S1 model teaches robots from one video

Skild AI co-founder and chief executive officer Deepak Pathak spoke about the race to general-purpose robots at TechCrunch Disrupt 2026 in San Francisco. He appeared on the Real World AI Industry Stage with Varun Jain, general partner at SE Ventures. Pathak came fresh from launching Skild's S1 model, which showed robots learning complex new tasks from a single video. The company has reached $100M in annual recurring revenue. The two discussed how quickly robots are learning to generalise, how robot intelligence scales, and where it creates value today. They closed with what people may be underestimating about the next five years of robotics.

Robotics Tomorrow
Sep 29th, 2026
Pittsburgh tech powers aviation industry innovation.

Pittsburgh tech powers aviation industry innovation. Homegrown Skild AI partners with PIT to test four-legged robots as part of latest xBridge innovation in the terminal. Already known globally as the robotics capital of the world, the Pittsburgh region is continuing to grow its tech prowess as part of its industry-leading new airport. More headlines. Articles. Pittsburgh International Airport's xBridge innovation program, which has worked with dozens of tech firms from start-ups to national companies, is once again serving as a real-world proving ground for emerging technology. Airport leaders announced two partnerships this week with companies testing cutting-edge products designed to enhance aviation industry efficiency, operations and customer service. Passengers may soon notice four-legged robots, or quadrupeds, strolling through parts of the Airside Terminal supporting terminal operations from monitoring indoor air quality to detecting spills and full trash bins among other uses. The robots, powered by Skild AI's adaptive autonomy software, are being evaluated for their ability to navigate complex airport environments and assist airport operations. Equipped with advanced sensors, cameras and AI-driven perception, the robot can roam autonomously, detect anomalies, and provide real-time data to operations staff. The announcements come as Pittsburgh is hosting the Intelligent Robots & Systems (IROS) Conference 2026, an international event with more than 70 countries represented. "Pittsburgh is a global leader in robotics and AI. Through xBridge, we're creating a place where that innovation can move from the lab into the airport environment and ultimately help shape the future of the industry," said Deepak Nayyar, Executive Vice President and Chief Technology Officer at PIT. "We're looking at how the latest technology can work alongside our staff and airline partners to enhance efficiency, improve operations and provide an additional level of customer service." Up to four autonomous robots and two charging stations will be deployed across the Airside Terminal. Each will have a defined area and keep-out zones. Skild AI will conduct the installation, mapping, supervision, and monitoring. They will also provide on-site training to PIT staff. "As a Pittsburgh company, we're proud to partner with xBridge to make Pittsburgh International a pioneer in airport robotics," said Sara Ahmadi, head of product at Skild AI. "We share the belief that robots will transform how public infrastructure operates, and we're grateful for the confidence they've put in Skild and the energy they bring to making that happen." The project will progress through multiple phases, allowing PIT and Skild AI to evaluate the technology across a range of airport functions. Skild AI was founded in 2023 in Pittsburgh by Deepak Pathak and Abhinav Gupta, both faculty members of Carnegie Mellon University's Robotics Institute and School of Computer Science. Pathak - the Raj Reddy Associate Professor at CMU - serves as chief executive; Gupta serves as president. The company's trajectory has been one of the fastest in the current AI cycle. Backed by SoftBank Group, NVIDIA Ventures, Macquarie Capital (entities administered by Macquarie Capital), Jeff Bezos, Sequoia Capital, Lightspeed, Coatue, Felicis, and others, the company is valued at over $14 billion. Environmental Monitoring The initial use case will focus on mobile indoor air-quality monitoring. Equipped with environmental sensors, a robot will autonomously collect location-specific data, monitor established thresholds and alert airport personnel when readings fall outside defined parameters. By automating routine data collection, the technology could help teams identify potential issues quickly. Supporting Terminal Operations The second phase will explore applications designed to complement the work of custodial and terminal operations teams. Potential use cases include identifying full or nearly full trash bins so employees can prioritize service, detecting potential spills to accelerate response and reduce safety hazards, and monitoring passenger congestion to give operations teams additional visibility into areas experiencing increased crowding. Advanced Applications The third phase will explore more advanced applications, including a potential partnership with BioFlyte, a former xBridge participant, to pair Skild AI's quadruped platform with BioFlyte's airborne biological threat detection technology. The combination could provide mobile biological monitoring beyond fixed sensor locations and give airport personnel another tool for identifying potential threats. The project will also evaluate the potential for autonomous delivery, including food and beverages, to select locations within PIT's airside terminal. If successful, autonomous delivery could also support employees by handling routine point-to-point transportation of items, allowing staff to remain focused on passengers and core operational responsibilities. "Pittsburgh is known globally for its growing tech economy, and our new industry-leading terminal continues to reflect that innovation right at the airport," said Allegheny County Executive Sara Innamorato. "The airport is an economic driver for the region and xBridge is another example of that success." For PIT's airline partners, the project offers an opportunity to explore how autonomous technology could work alongside the people who keep an airport operating efficiently every day. As part of a separate xBridge project, Arrow Analytics, in partnership with Southwest Airlines, is testing its EZ-Board technology in an active terminal environment to explore how the system can improve the boarding process and give airline teams better real-time information on how full overhead bin space is on the flight before a passenger boards. Airlines closely monitor boarding times because even a few minutes can affect aircraft schedules across an entire network. When overhead space becomes constrained, airlines may ask passengers to gate-check carry-on bags. PIT, Southwest Airlines and Arrow Analytics are exploring whether new technology can provide an even more precise, real-time view of carry-on baggage during boarding. Arrow Analytics has developed EZ-Board, an autonomous system that uses off-the-shelf depth cameras and real-time analytics to identify how carry-on bags move through the boarding process. The technology, installed at Gate A7 at PIT, gives airline gate agents and operations teams a real-time picture of baggage volume, while also helping predict how much overhead bin capacity will be needed. "We are excited to work with the innovative team at PIT as we continue to develop our EZ-Board product and computer vision offerings for airports and airlines," said Valerie McNeill, Chief Operating Officer of Arrow Analytics. Providing airline employees with better information about actual baggage volume and projected bin usage could increase boarding speed, reduce uncertainty and support more consistent, data-driven decisions.

The Next Web
Sep 10th, 2026
Skild AI hit $100M in run-rate revenue selling robots a brain that learns from one video.

Skild AI hit $100M in run-rate revenue selling robots a brain that learns from one video. Europe's Machinery Regulation starts covering machines with self-evolving behaviour on 20 January 2027, and a model that performs tasks absent from its training data is the case it was written around September 10, 2026 - 4:06 pm

Bloomberg
Sep 10th, 2026
Skild AI reaches $100M revenue run rate 10 months after launch

Skild AI, a startup developing software that helps robots learn tasks, has reached $100 million in recurring revenue run rate. The company achieved this milestone just 10 months after launching commercial operations. The fast-growing robotics firm's software enables robots to acquire new capabilities more efficiently. This rapid revenue growth demonstrates strong market demand for AI-powered robotic learning solutions.

TechRound
Aug 27th, 2026
Can single-video learning solve the biggest obstacle in commercial robotics?

Can single-video learning solve the biggest obstacle in commercial robotics? Les-Leigh A August 27, 2026 What if a robot could learn to do a job just by watching a ten-second video clip? Skild AI reckons its new S1 foundation model can pull off exactly that, tackling unfamiliar physical tasks without needing a single round of fine-tuning. Built on a large manipulation dataset, S1 adapts on the fly with no retraining, no tedious data collection and no post-deployment calibration required. If these claims survive the real world, it would conquer one of the biggest challenges in commercial robotics. Reprogramming hardware for every new task is slow, eye-wateringly expensive and requires a room full of specialists. A machine that learns simply by watching changes the economics math of it all. What S1 can do (and what still needs proving). At its heart, Skild is showing off one-shot imitation learning straight from video. S1 studies a brief clip of a human or robot doing a task, then builds a custom execution policy without needing any extra training. By pairing visual inputs with natural language context, its video-language architecture figures out what is happening on screen and turns it into real-world robotic movement. Skild released benchmarks showing S1 beating current state-of-the-art models on generalisation tasks, even when faced with workflows completely excluded from its training. The company's demonstrations include manipulation tasks across different object types, lighting conditions and surface textures. CEO Deepak Pathak, who previously led robotics research at Carnegie Mellon and Meta AI, has framed S1 as a step toward robots that can be launched in any environment a human can describe or demonstrate. These results come from Skild's internal testing, which is standard for an early launch, but it does mean independent labs haven't verified the claims just yet. The robotics research community has seen confident one-shot learning announcements before that performed well in curated demonstrations, but less so in the full range of real-world conditions. Transitioning from controlled manipulation benchmarks to active implementations in warehouses, hospital wards or construction sites involves real-world friction. Benchmarks intentionally strip away the unpredictability these real environments require would. Why one-shot learning is A big deal. Traditionally, teaching a robot means gathering hyper-specific data, running heavy supervised or reinforcement learning and locking in a model that only works in one set of conditions. Want to switch tasks? You are starting back at square one. That's why factory robots stick to one assembly line, logistics hardware stays on fixed paths and dynamic automation is wildly expensive for most operators. Large foundation models have shifted the trajectory for language and image processing. By training one large model on varied datasets, it learns broad representations that easily adapt to tasks it wasn't explicitly taught. Skild is now bringing this play to physical robotics: feed S1 enough varied interaction data so that it develops transferable skills, letting it master new workflows through simple demonstrations instead of costly retraining. Dataset scale is everything here. Skild's claim of having the largest manipulation dataset is important, because foundation model generalisation lives and dies by data diversity. Train on a narrow set of tasks, the robot stays narrow. Expose it to a wide spread of hardware types, objects, environments and movement structures - and it actually has something to draw on when facing something new. The real question is whether Skild's dataset is truly rich enough to deliver the seamless adaptability they have promised. The market impact. Reliable one-shot video learning radically lowers both the cost and complexity of hardware implementation. An operator who can teach a machine a new workflow just by filming a quick demonstration opens the door to environments that couldn't previously justify the high setup costs. Small-batch manufacturing, flexible warehouse setups, commercial kitchens and physical retail are all spaces where traditional robotics economics fell flat. The robotics startup space has been building toward this. Physical Intelligence, acquired by Google in late 2024, was working on similar generalisation problems. Figure AI, 1X Technologies and Agility Robotics are all investing heavily in manipulation capabilities. Skild's approach of publishing a foundation model instead of building a specific hardware product positions it differently - as infrastructure for the robotics industry, not just a robotics company itself. For now, S1 is an impressive research showcase. The road from a solid benchmark to a rock-solid industrial release is notoriously long, and the robotics sector is infamous for overpromising on timelines. What makes S1 worth tracking is whether scaling foundation models is the right blueprint for how the problem ultimately gets solved.