VP of AI

At Applied Computing, we’ve built Orbital, a physics-grounded AI copilot that operates inside heavy industrial systems such as refineries, upstream assets, and energy-intensive plants. Orbital combines real-time sensor data, physics-based models, and domain-trained language models to deliver interpretable predictions, anomaly detection, and optimisation recommendations in live production environments.

The VP of AI owns the direction, leadership, and commercial relevance of our AI function. This role exists to connect a deep understanding of industrial operations with what AI can deliver: identifying valuable problems, anticipating customer and market needs, and setting the priorities that move research and product development forward.

You will work alongside the CEO in customer and strategic discussions, represent AI at board level, and take responsibility for AI decisions, commitments, people, and budget. You must be able to answer “Can we do this?” with a credible view of the opportunity, approach, constraints, and path to delivery.

The Director of AI Research reports to you and leads technical research, development, and execution. Your responsibility is to give that team clear direction and protect its ability to deliver by owning the corporate, people, and customer responsibilities around it.

What You’ll Own

• AI strategy, innovation priorities, and the research agenda’s alignment with commercial needs

• Decisions about which industrial problems are worth solving and what solutions must achieve

• Customer, executive, and board-level communication on AI

• AI commitments and overall accountability for outcomes

• Leadership of the AI function, including hiring, budget, and people management

• The working relationship between AI, Product, and Technology

• Anticipating opportunities across refining, petrochemicals, and adjacent industrial sectors

Core Responsibilities

1. Set AI Direction Ahead of Customer Demand

• Translate company strategy, industrial needs, and commercial opportunities into clear AI priorities

• Identify emerging customer problems and steer research early enough to address them

• Balance near-term delivery commitments with investment in future capabilities

• Define the operational problem, intended users, required outcomes, and evidence of success for major AI initiatives

• Make clear decisions about what to pursue, defer, or stop

2. Bring Industrial Domain Judgement

• Understand how energy and industrial businesses operate, how users make decisions, and where technology can create meaningful value

• Distinguish technically interesting opportunities from problems customers will prioritise and adopt solutions for

• Bring practical judgement about operational constraints, deployment conditions, and user expectations into AI decisions

• Build on experience in refining, petrochemicals, or related energy domains, and develop expertise across adjacent areas such as upstream

• Keep research priorities grounded in how solutions will be used in real operating environments

3. Lead Customer and Commercial AI Discussions

• Partner with the CEO on customer calls, strategic relationships, and commercial opportunities

• Explain AI capabilities, limitations, and delivery approaches clearly to operational users, technical stakeholders, and executives

• Make informed feasibility decisions for established applications and approaches

• Work with the Director of AI Research to assess unfamiliar technical problems before making commitments

• Translate customer requests into well-defined problems and credible plans

• Maintain trust by surfacing uncertainty, dependencies, and trade-offs early

4. Lead the AI Function and Protect Execution

• Manage and support the Director of AI Research, who leads researchers and technical execution

• Own organisational planning, hiring priorities, budget, performance, and people responsibilities across the AI function

• Give the Director clear priorities, sufficient context, and the authority to lead technical delivery

• Handle stakeholder demands, corporate responsibilities, and escalations so researchers can focus on development and execution

• Resolve competing priorities and prevent unfiltered customer requests from disrupting agreed work

• Hold the function accountable for delivery, quality, and operational relevance

5. Turn AI Direction into Working Systems

• Work with the VP of Product and VP of Technology to deliver usable, reliable systems

• Establish clear responsibilities, decision processes, and working arrangements across the three functions

• Ensure AI priorities reflect product needs, engineering capacity, and deployment constraints

• Maintain oversight from problem definition through deployment and user adoption

• Address delivery risks and cross-functional dependencies early

• Represent AI strategy, progress, investment needs, and risks to the CEO and board

What Success Looks Like

First 30 Days

• Clear understanding of Orbital’s capabilities, limitations, research priorities, and customer commitments

• Working relationships established with key customers, the Director of AI Research, and the VPs of Product and Technology

• Day-to-day leadership of the Director transferred to you

• Active leadership of AI discussions alongside the CEO

• An agreed handover covering decisions, relationships, and outstanding commitments

By 60 Days

• Ownership of AI priorities, customer discussions, hiring, and budget

• A clear view of delivery risks and the decisions needed to address them

• Effective working arrangements across AI, Product, and Technology

• A prioritised research direction informed by customer needs and market opportunities

• The Director and researchers have clear focus and fewer avoidable interruptions

By 90 Days

• Independent day-to-day leadership of the AI function

• Ownership of executive and board communication on AI

• An agreed AI strategy connecting current delivery with future capabilities

• Customer commitments supported by credible scope, feasibility, and delivery plans

• Routine decisions and escalations handled without dependence on the outgoing AI leader

6–12 Months

• AI capabilities deployed and adopted against valuable industrial problems

• Research investment anticipates emerging customer needs

• Customers trust the company’s AI leadership and commitments

• AI, Product, and Technology operate effectively together

• A focused research organisation with clear priorities and strong technical leadership

• Growing ability to serve adjacent industrial domains

Must-Have Qualifications

• Strong understanding of oil and gas, petrochemicals, or the wider energy sector, including operational users and commercially valuable problems

• Experience taking industrial technology from problem identification through deployment and user adoption

• A track record of leading technical teams and owning strategy, hiring, and budget

• Sufficient understanding of AI and technology delivery to assess established applications, challenge assumptions, and recognise when deeper technical validation is required

• Experience leading customer discussions and translating operational needs into actionable technical priorities

• Ability to communicate clearly with industrial users, researchers, commercial leaders, and board-level stakeholders

• Sound judgement under ambiguity, with the confidence to make decisions and take responsibility for commitments

• Willingness to learn new industrial domains and travel to customers and sites

Nice to Haves

• Deep experience in refining or petrochemicals

• Experience delivering AI-enabled industrial solutions

• Exposure to upstream operations or other energy-intensive industries

• Experience leaading within a growing industrial technology business

How We Work

• Industrial value and user adoption guide our AI priorities

• We invest ahead of demand while remaining accountable for current commitments

• We make commitments with a clear understanding of feasibility and constraints

• We work with commercial teams and customers, to understand demand and future interests

• We give researchers clear direction and protect their ability to execute

• We surface risks early and take ownership of resolving them

• We judge progress by what works in production

What This Role Is Not

• Not a hands-on coding or individual research role

• Not a purely advisory position; this role owns decisions, people, budget, and commitments

• Not a customer-facing role disconnected from technical feasibility and delivery. Its both.

• Not a requirement to know every industrial domain on arrival; the ability and willingness to learn are essential

• Not a layer that passes requests through to researchers; this position must prioritise, decide, and lead