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