VP of AI

• 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