Program Manager - Flagship Programs
About the job
- 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.
About the company
Field AI builds autonomy software that lets mobile robots inspect and work in changing environments, including construction sites and industrial facilities. Its Field Foundation Models act as a shared control system for different machines, bringing perception, navigation and task planning together on the robot. Cameras and other sensors feed the system, while the platform sends site data into customers' project tools to track progress, record conditions and identify deviations from plans. The company sells commercial autonomy solutions and deployment services, integrating its software with robot manufacturers' equipment. Field AI was founded in 2023 and is headquartered in Irvine, California.
Company Size
201-500
Company Stage
Late Stage VC
Total Funding
$1.1B
Headquarters
Irvine, California
Founded
2023
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Simplify's Take
What believers are saying
- Industrial reach expands: September 2026 Caterpillar collaboration brings FieldAI autonomy into manufacturing and jobsite initiatives.
- Existing customers expand deployments: April 2026 Big-D Construction reporting describes broader FieldAI adoption after positive project feedback.
- Engineer pay includes ownership: FieldAI's Irvine behavior role lists $160,000 to $220,000 base salary plus equity participation.
What critics are saying
- Safety reduces speed: FieldAI says unfamiliar surroundings trigger slower movement or conservative routes, cutting throughput.
- Sales visibility is limited: October 2026 coverage combines FieldAI's $135 million in revenue and contracts without a breakdown.
- Irvine verification engineers must attend in person: FieldAI's live listing requires fully onsite work despite flexible hours.
What makes Field AI unique
- Uncharted navigation is built in: FieldAI says its robots operate without prior maps, GPS or cloud connections.
- Underground robotics shaped its leadership: FieldAI CEO Ali Agha led the team winning DARPA's urban underground challenge.
- Field data feeds testing: FieldAI converts robot mission data into simulated customer sites using NVIDIA Omniverse.
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Benefits
Hybrid Work Options
Growth & Insights and Company News
6 month growth
4%1 year growth
-3%2 year growth
32%Robot software and AI infrastructure pull nine-figure rounds in early october. Three rounds this week show investors moving capital toward the picks-and-shovels layer of physical AI - software, chips, and memory - rather than the robots and devices themselves. Robot software becomes a billion-dollar bet. FieldAI is set to close a $700 million round at a $10 billion valuation, according to The Next Web - a figure five times its mark from roughly a year ago [[1]]. The company sells what it calls a general-purpose brain for robots: software designed to run humanoids, drones, and rovers built by other manufacturers. FieldAI does not make hardware. Almost simultaneously, D-Robotics closed a $400 million Series C on September 17, led by Mirae Asset with strategic participation from Meituan and several Asian growth funds, as robotbelt.com reported [[3]]. D-Robotics also builds no robots. Its product is a chip - the Sunrise S600 - that combines brain and motor-control functions on a single die, positioning it against Nvidia's Jetson platform. Together, these two rounds argue that investors are pricing the orchestration layer of physical AI at a premium over the physical machines. The companies building the robots themselves may face commoditization pressure; the companies selling the software and silicon that make robots useful may not. Founders building in adjacent verticals - manufacturing automation, logistics, field operations - should register what this capital concentration signals about where durable margin is expected to land. An AI agent closes $1B in roughly one month. Instinct, a San Francisco personal-agent startup, closed a $1 billion Series C at a $10 billion post-money valuation on September 28, with Sequoia Capital, Benchmark, and Coatue leading, according to aisheetreport.com [[2]]. That represents a roughly four-times markup in approximately one month, an acceleration that is unusual even in a heated AI market. The product operates exclusively via SMS and phone calls - no app, no browser extension - and has reportedly booked half its $1 billion in gross merchandise volume through travel transactions. Access remains invite-only. The distribution model is notable: by routing through channels consumers already use daily, Instinct sidesteps the cold-start problem that kills most consumer AI products before they reach scale. For founders raising AI agent rounds right now, the Instinct data point cuts two ways. It demonstrates that tier-one firms will move extremely fast when conviction is high, compressing timelines that historically ran six to eight weeks. It also raises the bar: investors backing agents at these valuations are expecting GMV-scale evidence of real transactions, not user counts or session metrics. Photonics and specialized chips attract deep-tech capital. Volantis raised $88 million to build a photonic memory layer for AI inference, The Next Web reported [[5]]. The company uses gallium arsenide lasers rather than indium phosphide - a materials choice that distances it from Europe's dominant photonics infrastructure but also from the supply constraints tied to that ecosystem. The thesis is that moving data between compute and memory optically, rather than electrically, reduces latency and energy consumption enough to matter at inference scale. Whether that proves true in production is not yet clear from the available information. What is clear is that investors are funding multiple competing approaches to the same bottleneck - memory bandwidth - which suggests the problem is well-established even if the winning solution is not. Early-stage signal: CAD autocomplete finds 5,000 engineers. Against the nine-figure rounds, Hestus raised $7.4 million - a Y Combinator-backed seed - to build an AI copilot for CAD tools including Autodesk Fusion 360 and SolidWorks, as startuply.vc reported [[4]]. The company was founded by former Cruise engineers and has reached 5,000 mechanical engineer users. The product functions as autocomplete for parametric design: a user sketches a rough geometry and the system surfaces a fully constrained suggestion. The user base - mechanical engineers working in established professional software - is a high-value enterprise segment with clear workflow pain and demonstrable willingness to pay for productivity tools. The Hestus round is a useful reference point for pre-seed and seed founders. A specific, high-value professional segment, a concrete productivity primitive, a credible founding team, and early adoption numbers were sufficient to close capital. The round did not require a vision of replacing the underlying software - it required a believable wedge into it. What this week's rounds mean for your raise. The pattern across all five items is consistent: capital is moving toward infrastructure and enablement over end products. FieldAI and D-Robotics are enabling robots others build [[1]] [[3]]. Volantis is enabling inference others run [[5]]. Hestus is enabling engineers others employ [[4]]. Instinct is the partial exception - it is a consumer product - but even there, the distribution layer (SMS, phone) is infrastructure that sits beneath the application [[2]]. For founders raising a pre-seed or seed this quarter, the implication is that positioning your company as enabling an existing, large workflow tends to attract capital more reliably than positioning it as replacing the workflow entirely. Investors are pattern-matching on picks-and-shovels arguments across physical AI, enterprise software, and compute. That pattern is worth understanding before your next pitch. This week, if you are raising. * If you are raising in robotics or physical AI, reframe your pitch around the software or chip layer that makes hardware useful - investor appetite is concentrated there, not in the devices themselves. * If you are closing an AI agent round, prepare GMV-scale or transaction-level metrics; Instinct's $1B Series C signals that tier-one firms expect evidence of real economic activity, not engagement proxies. * If you are at pre-seed with a professional-software wedge, use Hestus as a comp: a specific user count in a high-value segment, a credible team, and a concrete productivity primitive were sufficient to close a seed - identify your equivalent proof points before approaching investors. Sources. physical ai ai agents deep tech seed funding
The startup, which is building a general-purpose "brain" for robots, has more than quadrupled its valuation in just over a year.
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.
Caterpillar and FieldAI advance ai-powered industrial innovation. Sep 02, 2026, 09:15 ET Leveraging robotics, digital twins and NVIDIA technologies, the collaboration helps create safer, smarter and more productive operations IRVING, Texas, Sept. 2, 2026 /PRNewswire/ - As industries face growing labor shortages and increasing productivity demands, Caterpillar Inc. (NYSE: CAT) is collaborating with FieldAI to advance physical AI, autonomy and robotics that help jobsites and factories operate safer and more efficiently. By turning real-time observations into actionable insights, physical AI helps improve decision-making, enhance safety and maximize the effectiveness of every worker and machine. "The future of our industries will be shaped by how effectively we combine human expertise with AI-powered machines," said Jaime Mineart, Caterpillar chief technology officer. "Collaborations like this help accelerate the journey. We're focused on applying AI where it matters most, helping customers improve safety, increase productivity and get more value from every operation." Earlier this year at CES, Caterpillar shared its vision for the jobsite of the future: a safer, more productive and easier-to-run operation powered by intelligent machines, connected workflows and digital insights. This collaboration combines Caterpillar's deep industry expertise, engineering capabilities and operational data with FieldAI's AI-enabled robot foundation models to autonomously operate across complex jobsites and manufacturing environments, accelerating practical solutions that improve safety, productivity and operational efficiency. Early applications include: * Autonomous inspections to improve safety and operational visibility. * Jobsite and facility digital twins that provide real-time insights into equipment, infrastructure and operations. * Enhanced situational awareness to help identify potential risks sooner and support faster, more informed decision-making. * Operational optimization that improves efficiency through simulation, automation and AI-driven insights. "Modern manufacturing at Caterpillar is transforming how we work, from design to delivery, through advanced technology, digital innovation and a culture of continuous improvement," said John Tuntland, Caterpillar senior vice president of Integrated Components Division. "These technologies give our teams greater visibility into how our facilities operate and help us identify opportunities to improve safety, optimize flow and make more informed decisions. As our people embrace new ways of working, we are building manufacturing operations that respond faster to change and perform at their best." Caterpillar selected FieldAI for its proven ability to deploy in complex, dynamic industrial environments where traditional automation often falls short. FieldAI's robot-agnostic autonomy and foundation models can turn large volumes of operational and jobsite data into actionable insights - complementing Caterpillar's manufacturing modernization and jobsite of the future initiatives. "Caterpillar has shaped operations on jobsites and manufacturing facilities for over a century, and these are exactly the environments FieldAI excels in, with deployments spanning hundreds of sites worldwide," said Ali Agha, founder and CEO of FieldAI. "This collaboration brings leading physical AI capabilities to a leading manufacturer of construction and mining equipment. Together, we are shaping the next century of AI-enabled heavy industry." Caterpillar and FieldAI are advancing AI-powered autonomy to help customers build, move and power progress more safely, productively and efficiently. Leveraging NVIDIA accelerated computing, NVIDIA Omniverse technologies and high-fidelity digital twins built from operational data, the collaboration improves site visibility, accelerates decision-making and supports the next generation of industrial operations. About Caterpillar For more than a century, Caterpillar has built a better, more sustainable world. With 2025 sales and revenues of $67.6 billion, Caterpillar Inc. is shaping the future as the world's leading manufacturer of construction and mining equipment, off-highway diesel and natural gas engines, industrial gas turbines and diesel-electric locomotives. Backed by one of the largest independent global dealer networks and financing services through Cat Financial, the company's primary business segments: Power & Energy, Construction Industries and Resource Industries are solving customers' toughest challenges through commercial excellence and advanced technology, driven by a highly skilled, dedicated global team. Learn more at www.caterpillar.com. About FieldAI FieldAI is the leader in deploying general-purpose robots in unstructured environments, providing frontier autonomy software as a universal "brain" that works across many robots and many tasks. The company's Field Foundation Models(TM) uniquely combine data-driven AI with physics-based reasoning and uncertainty awareness, enabling safe and reliable operations in environments previously considered too unpredictable for robots. FieldAI's leadership team brings together world-class expertise in AI foundation models and real-world robotics, with roots in Google DeepMind, NASA JPL's Mars exploration program, and DARPA's Subterranean Challenge and RACER programs. The company has raised over $400 million from investors including Bezos Expeditions, Emerson Collective, Khosla Ventures, Canaan Partners, NVentures (NVIDIA's venture capital arm), Prysm Capital, Temasek, and others. For more information, visit www.fieldai.com or contact [email protected]. SOURCE Caterpillar Inc.