AI Product Manager – Transportation

Overview

In this role you’ll guide AI-enabled transportation solutions that help engineers design faster, with lower cost and greater certainty. You will own the AI product roadmap, translating transportation expertise into deployable products used on real projects. You’ll lead cross-disciplinary teams to explore large solution spaces and compare options across cost, carbon, risk and performance. Your work will connect client value with robust, repeatable AI capabilities deployed globally, shaping how infrastructure is planned and engineered.

Pay / Benefits

• medical, dental, vision benefits
• life and disability insurance
• retirement savings plan
• employee stock purchase plan
• paid time off
• flexible work options

Responsibilities

• Own and evolve the AI-enabled transportation product roadmap, prioritising high-value problems.
• Engage with transportation teams and clients to surface where cost, time, uncertainty, and effort are concentrated.
• Translate domain knowledge into technically grounded product requirements for ML/RL researchers and software engineers.
• Lead ML/RL and software engineering teams to deliver working capabilities against milestones.
• Develop products enabling exploration of large solution spaces across cost, carbon, risk, constructability and performance.
• Define how uncertainty and confidence levels are represented to support defensible engineering decisions.
• Define measurable success outcomes (design quality, speed, cost, material reduction, adoption).
• Test products on real transportation projects and iterate from engineer and client feedback.
• Represent the Transportation AI roadmap in discussions with market, technical, digital and executive leadership.
• Transition prototypes into robust, repeatable products deployable globally.

Key requirements

• Deep desire to challenge and transform transportation engineering beyond digitisation.
• Experience in transportation infrastructure including planning, design, engineering, modelling or network analysis.
• Technical fluency in modern AI/ML approaches suitable for collaboration with ML/RL researchers, data scientists, and engineers.
• Strong understanding of engineering decisions: constraints, uncertainty, trade-offs and validation.
• Ability to translate domain needs into actionable technical requirements.
• Experience communicating with technical, client and executive audiences.

• Strong communication and decision-making
• Stakeholder alignment
• Cross-functional collaboration
• AI/ML proficiency for product context (ML/RL)
• Reinforcement learning, optimisation, probabilistic methods
• Experience with GenAI or LLM-based workflows

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