The Senior AI Research Pod Leader translates enterprise and product strategy into research agendas and executable technical plans, ensures adherence to best practices and Responsible AI principles aligned to Caterpillar Values, and mentors pod members to build deep technical, scientific, and professional capability. The role works closely with AI Product Owners, AI Architects, engineering partners, the IT organisation, universities and academic collaborators, and business stakeholders to explore, validate, and transition advanced AI concepts that can deliver future business value.

Responsibilities Include but not limited too.

Research & Technical Leadership

• Own the technical and research direction for the pod, spanning data science, machine learning, and advanced AI research, including feature engineering, model and algorithm selection, experimental design, evaluation, and transition approaches.

• Define and guide research programs and experiments, balancing hypothesis‑driven exploration with enterprise relevance and downstream applicability.

• Ensure solutions and research outcomes are scalable, maintainable, and aligned with enterprise architecture, data, security, and engineering standards.

• Review, challenge, and approve technical designs, experiments, research findings, and AI artefacts produced by the pod.

• Drive efficiency and quality through the adoption of Generative AI tooling, automation, reusable research patterns, and shared platforms.

• Establish expectations for scientific rigor, documentation, reproducibility, and evaluation consistency.

Delivery, Experimentation & Transition

• Lead pod‑level planning and execution across research and delivery activities, aligning exploration, experimentation, and milestones to the broader product, technology, and IT roadmaps.

• Balance experimentation with execution, ensuring effective transition from research and proof‑of‑concept work to production‑ready or downstream engineering solutions.

• Partner with the IT organisation to ensure research outcomes can be operationalised, supported, and scaled within enterprise platforms and environments.

• Actively identify, manage, and communicate technical risks, research uncertainties, dependencies, and trade‑offs.

• Maintain accountability for outcomes, including research quality, delivery commitments, technical debt management, and operational readiness where applicable.

People & Research Capability Leadership

• Provide day‑to‑day and senior‑level technical leadership, coaching, and mentoring for pod members.

• Support development of skills across data science, machine learning, Generative AI, advanced AI research methods, and software engineering practices.

• Foster a culture of collaboration, accountability, learning, peer review, and continuous improvement within the pod.

• Contribute input into performance conversations through technical and research‑focused feedback and capability assessment.

Stakeholder, IT & Academic Collaboration and Thought Leadership

• Partner closely with AI Product Owners to translate business problems and long‑term strategy into clear research themes, analytical objectives, and value hypotheses.

• Collaborate closely with the IT organisation to align AI research initiatives with enterprise platforms, data foundations, security, infrastructure, and operating models.

• Influence and inform IT architecture, tooling, and platform strategy based on emerging AI research needs and future‑state requirements.

• Work with IT delivery, operations, and platform teams to establish pathways from research to supported enterprise solutions.

• Collaborate with universities and academic research institutions to shape joint research agendas, co‑supervise research projects, and accelerate exploration of frontier AI topics.

• Support and sponsor joint research initiatives, including funded research programs, student projects, internships, and post‑graduate collaborations where appropriate.

• Engage with academic partners on publications, conferences, workshops, and research dissemination, balancing openness with enterprise IP and confidentiality requirements.

• Communicate technical and research concepts, progress, risks, and outcomes effectively to non‑technical and executive stakeholders.

• Collaborate with platform, data engineering, MLOps, software engineering, architecture, and IT operations teams to influence future AI platforms and capabilities.

• Contribute to internal and external thought leadership, including technical documentation, whitepapers, patents, and research publications where appropriate.

Quality, Governance & Responsible AI

• Ensure models, research outputs, and AI solutions meet quality, performance, security, reliability, and compliance standards.

• Apply Responsible AI principles consistently across research, experimentation, IT collaboration, academic collaboration, and delivery lifecycles.

• Ensure appropriate documentation, traceability, and governance for research artefacts, experiments, and decisions.

Continuous Improvement & Innovation

• Stay current with advances in data science, machine learning, Generative AI, and emerging AI research areas.

• Leverage insights from academic, IT, and industry research communities to inform enterprise AI strategy and practice.

• Recommend improvements to tools, processes, and standards across the data science, AI research, and IT enablement practices.

• Contribute to communities of practice, shared standards, and reusable assets across the enterprise.

Qualifications & Education

• Bachelor’s degree in data science, computer science, engineering, mathematics, statistics, or a related field (or equivalent practical experience). 

• Advanced degree (Master’s or PhD preferred) in artificial intelligence, machine learning, engineering, mathematics, physics, or a closely related field, or equivalent depth of practical research experience is required.

Skill Descriptors

Advanced AI & Research Expertise

• Strong understanding of AI, machine learning, and Generative AI concepts, risks, and opportunities.

• Demonstrated depth in one or more advanced or emerging areas such as multimodal models, embodied or physical AI, reinforcement learning, simulation‑based learning, or quantum‑inspired algorithms.

• Ability to design, interpret, and guide experiments and model evaluations to inform technical and research decisions.

Product, IT & Strategy Acumen

• Ability to translate business, technology, and IT strategy into outcome‑driven research themes, product goals, and measurable value hypotheses.

• Experience balancing long‑term research investment with near‑term delivery and enterprise operability.

Agile & Delivery Ways of Working

• Experience working in Agile or iterative delivery environments, including sprint planning, backlog refinement, and incremental delivery.

• Ability to balance structured delivery with exploratory research work.

Communication

• Excellent verbal and written communication skills, with the ability to explain complex technical and research concepts to non‑technical stakeholders.

Problem Solving & Systems Thinking

• Strong problem‑solving skills with the ability to think critically and creatively.

• Systems‑level thinking across algorithms, data, software, infrastructure, and operational contexts.

Programming & Technical Literacy

• Knowledge of relevant programming languages, tools, and prompt engineering.

• Programming literacy sufficient to collaborate effectively with engineering and IT teams; hands‑on coding is not a primary responsibility but technical credibility is expected.

Ethics & Responsibility

• Strong understanding of AI risks, limitations, and ethical considerations.

• Ability to govern and guide AI research and solution development in line with Responsible AI principles.

Compensation & Benefits:
Competitive salary based on degree and professional industry working experience.  The Total Rewards package includes:

• Competitive remuneration package

• Attractive Bonus and Share options

• Career development with global prospects

• A strong commitment to safety and your wellbeing

• An inclusive workplace culture focused on quality, customer service and the environment

• A commitment to diversity and inclusion, equal opportunity, and equal outcome

• SMART spending APP

• The opportunity to do truly meaningful work in a supportive, constructive culture that encourages you to make the most of your talents.

Additional Information:

Caterpillar of Australia is not currently hiring individuals for this position who now or in future require sponsorship for employment-based non-immigrant and immigrant visas. However, as a global company, Caterpillar offers many job opportunities outside of Australia which can be found through our employment website http://www.caterpillar.com/careers.

Your road to success begins with a Caterpillar career. By joining the Caterpillar team, you’ll discover that working for a global leader creates endless opportunities for you.