Full-Time
Program Manager
Flagship Programs
Updated on 9/3/2026
Field AI
201-500 employees
Hardware-agnostic AI platform for robots
No salary listed
Irvine, CA, USA
In Person
Bachelor's
 Project & Program Management (1) |
|---|
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- 10+ years of project or program management experience, including significant responsibility for large, complex, customer-facing technology programs.
- Demonstrated success delivering projects on time and within budget while managing multiple teams, workstreams, and organizational dependencies.
- Experience working at a technology company, preferably in enterprise software, robotics, artificial intelligence, industrial technology, automation, or another technically complex environment.
- Proven ability to engage and influence executive-level customer stakeholders.
- Experience coordinating technical delivery teams and providing leadership to project managers or other cross-functional workstream owners.
- Strong understanding of product-development and technology-delivery lifecycles.
- Exceptional written, verbal, and presentation skills, with the ability to communicate complex information clearly and concisely.
- Strong financial and commercial judgment, including experience managing program budgets, scope changes, and contractual deliverables.
- Excellent organizational skills and the ability to bring structure, clarity, and accountability to ambiguous or rapidly evolving situations.
- Professional presence, sound judgment, and a customer-oriented mindset.
- Ability to influence and drive results across teams without direct authority.
- A bachelor's degree in business, engineering, computer science, or a related field, or equivalent professional experience.
- Lead the planning and execution of large, complex customer programs from initial scoping through development, deployment, and operational handoff.
- Serve as the primary program-level interface between executive customer stakeholders and Product, Solutions, Engineering, and Delivery organizations.
- Translate customer business objectives into clear program requirements, priorities, milestones, and success criteria.
- Develop and maintain integrated program plans covering scope, schedules, budgets, resources, dependencies, risks, and deliverables.
- Indirectly lead and coordinate a team of Technical Project Managers, ensuring that individual workstreams remain aligned with overall program objectives.
- Establish effective program governance, including executive steering meetings, operating reviews, status reporting, decision-making processes, and escalation paths.
- Communicate program progress, risks, tradeoffs, and decisions clearly to executive and technical audiences.
- Proactively identify and resolve execution challenges, cross-functional dependencies, resource constraints, and changes in scope.
- Partner with Product and Engineering leaders to align customer commitments with product roadmaps, technical feasibility, and organizational capacity.
- Manage customer expectations while maintaining strong relationships and a high standard of professionalism.
- Monitor project financial performance and ensure programs are delivered on time, within budget, and in accordance with contractual commitments.
- Drive accountability across internal and external teams without relying solely on direct reporting authority.
- Capture lessons learned and continuously improve enterprise program-management practices, tools, and processes.
- Support the expansion of successful customer programs from initial deployments into broader enterprise-scale adoption.
- Experience delivering technology solutions for customers in energy, manufacturing, construction, logistics, automotive, mining, or other industrial sectors.
- Experience with enterprise robotics, autonomous systems, artificial intelligence, or hardware-and-software deployments.
- Experience managing programs that include product development, systems integration, field deployment, and organizational change.
- Familiarity with industrial operating environments, safety requirements, and customer procurement or governance processes.
- Project or program management certification, such as Project Management Professional, Program Management Professional, or an equivalent credential.
Field AI provides a hardware-agnostic AI platform that lets robots operate autonomously in real-world, unstructured environments. Its Field Foundation Models (FFMs) act as a universal AI brain that can run on different robots, from autonomous vehicles to humanoids, to automate tasks on construction sites, in oil and gas, and during site inspections. The FFMs serve as a shared core for perception, planning, and control and can be integrated with existing equipment, while partnerships with other companies expand the available solutions. The goal is to speed up the adoption of autonomous robotics in industries where reliable operation in unpredictable settings is essential, by offering interoperable AI and an open collaboration model.
Company Size
201-500
Company Stage
Late Stage VC
Total Funding
$405M
Headquarters
Irvine, California
Founded
2023
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Simplify's Take
What believers are saying
- Big-D expanded FieldAI after two years of pilots, signaling enterprise conversion.
- Ouster collaboration on June 2, 2026 strengthens rugged sensing for unmapped environments.
- Innovo, McLaren, and Caterpillar broaden FieldAI’s deployment base across construction and industrial sites.
What critics are saying
- FieldAI still lacks disclosed revenue mix, retention, and gross margin data.
- Safety failures on Caterpillar jobsites or McLaren sites trigger shutdowns and liability exposure.
- Boston Dynamics, Ouster, and OEM autonomy stacks squeeze FieldAI before 2027 scaling.
What makes Field AI unique
- FieldAI’s FFMs run across Spot, humanoids, and quadrupeds without hardware lock-in.
- Caterpillar partnered with FieldAI on September 2, 2026 for industrial physical AI.
- FieldAI already runs across hundreds of sites in construction, mining, and data centers.
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Benefits
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6 month growth
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↑ 30%Caterpillar and FieldAI develop industrial physical AI. September 3, 2026 Caterpillar and FieldAI will develop physical AI for industrial operations. Initial applications span autonomous inspections, digital twins, situational awareness, and manufacturing optimisation. Caterpillar is collaborating with FieldAI to develop physical AI, robotics, autonomy, and digital-twin applications for factories and industrial jobsites. The programme combines Caterpillar's engineering expertise and operational data with FieldAI's robot-agnostic foundation models, with initial applications covering autonomous inspections, facility visibility, situational awareness, and operational optimisation. The collaboration puts artificial intelligence into an environment considerably less forgiving than the office applications that have dominated much recent investment. Factories, mines, construction sites, and equipment yards contain moving machinery, people, changing surfaces, restricted areas, weather, dust, and incomplete information. An AI system operating in those surroundings must do more than produce a plausible answer; its decisions have to remain predictable enough to coexist with equipment capable of causing physical damage when something goes wrong. FieldAI develops autonomy software intended to operate across different robotic platforms rather than being built around one machine. Caterpillar says the company's foundation models combine large amounts of operational data with an ability to work across complex and changing industrial environments where traditional automation can struggle. FieldAI already has deployments spanning hundreds of sites worldwide, giving the partnership a base of operating experience rather than beginning with a laboratory prototype. Autonomous inspection is one of the first applications. Mobile robotic systems can collect repeated observations in areas where manual inspection is hazardous, time-consuming, or difficult to schedule, giving engineering teams more frequent information without requiring a person to make every data-gathering visit. That does not remove the maintenance decision from human operators, but it can shift labour away from routine collection towards interpreting exceptions and planning the work needed to correct them. Digital twins form another part of the programme. Caterpillar and FieldAI intend to use operational information to build models of jobsites and manufacturing facilities that provide current visibility of equipment, infrastructure, and activity. Those models can then support simulation and analysis before a layout, work sequence, or production process is changed physically, reducing the amount of experimentation that has to take place around live operations. Caterpillar already has a substantial programme in that area. In January, the manufacturer expanded its work with NVIDIA and said it was building physically accurate factory digital twins using Omniverse libraries and OpenUSD. Its manufacturing data platform is also being used with an NVIDIA AI Factory to support activities including forecasting and scheduling. The FieldAI relationship therefore adds robotic perception and autonomy to an existing manufacturing digitalisation programme rather than creating an entirely new technology direction. The additional capability is particularly relevant where fixed automation is difficult to justify. Traditional industrial robots perform well when the process, geometry, and work area can be tightly controlled, but jobsites and large factories contain activities that move over time or take place in spaces designed primarily for people and mobile equipment. A robot capable of adapting to those conditions could address tasks that would otherwise remain manual because conventional automation requires too much dedicated infrastructure. Adaptability comes with a different validation burden. Highly constrained automation can be inflexible precisely because its permitted behaviour has been engineered and tested in detail. A general-purpose robotic system is expected to cope with more variation, increasing the importance of uncertainty awareness, fail-safe behaviour, communications resilience, supervision, and clear limits on what the machine is allowed to do when its observations do not match previous experience. FieldAI says its models combine data-driven AI with physics-based reasoning and uncertainty awareness, and Caterpillar selected the company for its ability to deploy in dynamic industrial environments. The robot-agnostic approach could allow similar autonomy capabilities to operate across several machine types, but software portability does not remove the physical differences between platforms. Payload, dimensions, sensor position, stopping distance, ground conditions, and available actuation still define what any individual machine can safely achieve. Caterpillar's own factories give the partnership a useful proving ground. The manufacturer can test inspection, digital-twin, and situational-awareness systems around production operations where process data and engineering support are already available before attempting broader deployment across less controlled customer environments. The company has said improved visibility is intended to help production teams identify opportunities to improve safety and optimise flow, tying the work directly to manufacturing rather than treating physical AI solely as a future equipment feature. The scale of Caterpillar's industrial footprint makes that potentially significant. The company reported 2025 sales and revenues of $67.6 billion and manufactures construction and mining equipment, engines, industrial gas turbines, and diesel-electric locomotives. Its machines operate through a global customer and dealer network, so even selective adoption of inspection robotics or autonomy tools would expose the technology to a much broader range of working conditions than a single demonstration site. The collaboration will also use NVIDIA accelerated computing and Omniverse technologies, allowing digital models and robotic systems to draw on the same broader infrastructure Caterpillar is already developing for manufacturing and autonomous equipment. That common technology base may help move applications between simulation and physical deployment, although real operating data will remain essential if models are to reflect changing sites rather than idealised digital versions of them. No commercial rollout schedule, number of robots, factory deployment count, or financial value has been disclosed. The announcement should therefore be treated as a development collaboration rather than evidence that physical AI has already been deployed across Caterpillar's manufacturing system. Those missing figures also provide useful markers for what needs to come next: named applications, operating hours, safety performance, productivity data, and evidence that autonomous systems remain dependable when industrial environments change around them. Physical AI has accumulated much of the familiar technology-sector enthusiasm, but Caterpillar's involvement puts the idea against production lines, heavy machinery, and jobsites where capability is measured through uptime, safety, and output. FieldAI brings autonomy technology already used across hundreds of sites; Caterpillar brings factories, machines, engineering data, and industrial scale. The collaboration becomes materially important if those ingredients produce systems that perform repeatable work without requiring the real world to be rearranged for the robot first. Stories for you. * AI tops pharma supply chain investment priorities AI investment now dominates pharmaceutical supply chain technology priorities. LogiPharma research finds 96% of surveyed leaders rank AI and machine learning among their leading investment areas. * Caterpillar and FieldAI develop industrial physical AI Caterpillar and FieldAI will develop physical AI for industrial operations. Initial applications span autonomous inspections, digital twins, situational awareness, and manufacturing optimisation.
Caterpillar Inc. has announced a collaboration with FieldAI to deploy artificial intelligence, robotics, and digital twin technology across industrial jobsites and manufacturing facilities. The partnership combines Caterpillar's operational data and engineering capabilities with FieldAI's robot-agnostic autonomy software and foundation models. Initial applications include autonomous inspections, real-time digital twins of facilities, and situational awareness tools. The companies are using Nvidia accelerated computing and Nvidia Omniverse technologies to develop high-fidelity digital twins based on operational data. FieldAI has raised more than $400 million from investors including Bezos Expeditions, Khosla Ventures, Temasek, and NVentures. Caterpillar reported sales and revenues of $67.6 billion for 2025. No financial terms for the collaboration were disclosed.
Robot.com unveils R-dog: A four-legged Robotic platform Designed to deliver, advertise, and engage. Aug 11, 2026, 12:00 ET Designed to run on REMI, Robot.com's fleet management and physical API layer, the concept robot will evolve into a production version through an expanded collaboration with FieldAI, whose general-purpose Field Foundation Models will serve as the robot's autonomy brain. SAN FRANCISCO, Aug. 11, 2026 /PRNewswire/ - Robot.com(R), the company putting robots to work in the real world, today unveiled R-dog(TM), its new concept for a four-legged delivery robot built for two distinct and essential roles in robotics: completing the last 100 meters where wheeled robots cannot operate, and turning those same locations into interactive advertising. R-dog will run on REMI, Robot.com's fleet management platform and the physical API layer that integrates, controls, and orchestrates every robot in the company's fleet. Every delivery eventually reaches the hard part: stairs, curbs, thresholds, uneven ground between a lobby and a door, and many other impediments. While wheeled robots stop there, R-dog was designed as a solution for those last few steps. Its four legs carry it up and down stairs, over curbs and thresholds, and across the mixed terrain where the final stretch of a delivery actually happens, including dense urban streets, residential neighborhoods, and campus settings such as universities, hospitals, and corporate or industrial sites. The R-dog concept is built for real payloads, featuring a large onboard compartment that holds multiple items at once. For example, stacked food orders alongside standard e-commerce parcels, so the same robot handles food delivery and package delivery without a hardware change. When it arrives, unattended auto drop-off completes the handoff and sends the R-dog to its next assignment. A Delivery Robot That Turns Advertising Into Something People Walk Toward R-dog's second application is interactive advertising through R-ads(TM), Robot.com's unified, self-serve advertising suite. The same expressive, approachable design that draws people toward the robot also turns it into a moving, interactive out-of-home ad platform. Brands will be able to remotely upload and manage campaigns to the R-dog's dual video screens, and let the robot's personality do the work. People notice it, walk toward it, interact with it, and then notice the brand behind the experience. An impression that begins with a smile converts in ways that static signage cannot. "A decade of building delivery robots has taught us that most robots hit a wall in the final 100 meters, leaving a person to finish the job by hand. R-dog is our answer to that," said Felipe Chavez Cortes, CEO and Co-Founder of Robot.com. "And we're equally proud of how we built it. REMI is the operating foundation that runs every robot we have. When we wanted to add a quadruped, we didn't need to build a new operational system from the ground up. We built R-dog to run on what already exists, and that is what makes launching a new robot form factor possible at the pace we move." The Platform & Partner Backing the Bot Every Robot.com robot is connected through REMI, the company's physical API platform and fleet management layer that manages everything from teleoperation to performance and deployment. Work across every category, from delivery robots to humanoids and four-legged robots, contributes to shared intelligence that improves future tasks across the fleet. This foundation allows Robot.com to deploy new form factors without rebuilding from scratch. As R-dog moves toward production, Robot.com is expanding its collaboration with FieldAI. Field Foundation Models (FFMs) serve as an operational AI layer that generalizes across robots and environments and serves three roles: enabling safe and reliable operations in dynamic, real-world spaces without prior information or supporting infrastructure; preventing model hallucinations through physics-grounded AI models; and coordinating multiple robots working together. "Every delivery ends where Field Foundation Models are strongest, in the dynamic, unpredictable last 100 meters where the map runs out," said Ali Agha, Founder and CEO of FieldAI. "Grounded in physics and uncertainty quantification, FFMs enable R-dog to adapt to those conditions in real time and operate with the safety, reliability, and interpretability required for real-world deployment." The Newest Robot in an Expanding Fleet The R-dog concept joins an expanding portfolio that includes R-kiwi(TM) for campus delivery, R-cargo(TM) for industrial and warehouse logistics, R-noid(TM) for humanoid labor solutions, and R-ads across the fleet. More than 500 robots are currently deployed across the United States, Canada, Dubai, and MENA, having completed more than 2.5 million tasks to date. R-dog launches today as a concept, with commercial deployments in 2027. About Robot.com Robot.com Holdings Inc., doing business as Robot.com(R), is a pioneer in practical robotics solutions powered by advanced AI. The company operates a dual-engine business: Robotic Services, delivering Level 4 autonomous robots for campus delivery, warehouse logistics, and inspection; and Robot.com Media, a national OOH advertising platform powered by its mobile robot fleet. With more than 500 robots deployed across the United States, Canada, Dubai, and MENA, and completing over 2.5 million tasks, Robot.com operates at enterprise scale every day in real environments. Founded in 2017 and headquartered in San Francisco with offices in Colombia, Robot.com partners with enterprise operators, including Sodexo, to solve workforce and logistics challenges today. Robot.com is a founding member of Robots for America, a national coalition advancing the adoption of robotics and American industrial competitiveness. Forward Looking Statements This press release contains certain forward-looking statements within the meaning of the Private Securities Litigation Reform Act, as amended, including those relating to certain industry metrics, Company performance metrics and other statements that are predictive in nature. These statements relate to future events, future expectations, plans and prospects. These statements may be identified by the use of forward-looking expressions, including, but not limited to, "expect," "anticipate," "intend," "plan," "believe," "estimate," "potential," "predict," "project," "should," "would" and similar expressions and the negatives of those terms. Although the Company believes that the expectations reflected in such forward-looking statements are reasonable as of the date made, actual results or outcomes may prove to be materially different from the expectations expressed or implied by such forward-looking statements. Meaningful factors that could cause actual results to differ include, but are not limited to, whether we will have adequate financial resources to enable us to pursue our business successfully, given that we will likely need more financial resources than the additional resources. These statements are only predictions and involve known and unknown risks, uncertainties, and other factors. The Company does not undertake any obligation to release publicly any revisions to forward-looking statements as a result of subsequent events or developments, except as required by law. For Investors: Matt Kreps Darrow Associates +1-214-597-8200 [email protected] SOURCE Robot.com
McLaren teams up with US AI firm to bring robots to site. Initiative marks California company's debut in UK McLaren has teamed up with US firm FieldAI to bring general purpose robots to its construction sites. Under the partnership, McLaren will deploy autonomous quadruped robots, initially to capture 360° site imagery, generate point cloud data and support progress verification, model-to-site deviation analysis, safety compliance patrols and quality assurance. Regular automated scans by California-based FieldAI robots will create a visual and spatial record of work as it is installed. AI-enabled deviation analysis will compare site data against the design model, reducing the time between install and identification of on-site quality issues. The partnership marks FieldAI's entry into the UK market with the firm already working at hundreds of sites across Europe, Asia, and North America. McLaren Construction's group pre-construction director Adam Nicholson said: "The significance of this deployment for the construction industry is that we now have autonomous robots navigating stairs, doors and other obstacles and constantly working with our human teams to support productivity, safety and quality."
FieldAI, a robotics startup building software to help robots navigate challenging environments, has reached $100 million in revenue and customer contracts. The company, founded in 2023, serves 30 customers across Europe, Asia and the US in construction, data centres and defence. FieldAI develops a "universal general-purpose brain" that works across various robots, from humanoids to drones, enabling autonomous operation in unpredictable settings. Last year, the startup raised $405 million from investors including Jeff Bezos' family office, Emerson Collective, Khosla Ventures and Nvidia's venture arm, valuing it at $2 billion. The company's software uses physics and probability to build "risk-aware" AI that requires less training data than competitors. CEO Ali Agha previously spent seven years at NASA's Jet Propulsion Laboratory developing autonomous systems.