Head of AI Research Product

Via is a global leader in smart transportation, using technology to revolutionize mobility worldwide. Our impact ranges from transforming individual daily commutes to reducing the collective environmental cost of transit systems. We're driven by ambitious goals. This is an opportunity to join a core team reshaping how public mobility operates at scale.

We are looking for a Head of AI Research Product to lead the intersection of applied machine learning research and product innovation across Via's rider and transit operations platform. This role sits at the frontier of what AI can do for public transportation, not as a feature layer, but as the engine that makes the system smarter, more equitable, and more efficient for every rider and agency we serve.

This is a three-part role: innovating with lighthouse customers to pilot and pressure-test cutting-edge approaches in real-world transit environments, conducting original applied research, and driving productization of proven methods into Via's core platform.

What you'll do:

• Build and lead Via's AI research product function, defining the roadmap for how machine learning advances rider experience and transit network performance.
• Design and deploy reinforcement learning systems that optimize routing, pooling efficiency, and real-time dispatching, balancing rider satisfaction against vehicle utilization and transit agency cost targets.
• Pioneer AlphaGo-style machine learning for transit: combine deep reinforcement learning, and tree search over high-fidelity simulations of real transit networks, training policy and value networks that discover dispatching, pooling, and network-design strategies beyond what hand-built heuristics or conventional optimization can reach.
• Develop weighted ensemble models and multi-objective optimization frameworks that surface the right tradeoffs between competing system goals: wait times, cost per ride, emissions, and equity of service.
• Invent personalization systems that adapt the rider mobile experience in real time, surfacing relevant trip options, proactive alerts, and dynamic pricing signals, in close partnership with the rider mobile product team.
• Research and productize behavioral incentive models that increase ridership and shift demand to improve aggregation: surge smoothing mechanisms, loyalty structures, and gamified engagement grounded in behavioral economics and causal inference.
• Partner with lighthouse transit agencies to co-develop and validate novel AI approaches, turning field pilots into scalable platform features.
• Establish rigorous experimentation infrastructure (A/B testing, contextual bandits, offline policy evaluation) to measure the real-world impact of model changes on rider behavior and agency outcomes.

What we are looking for:

• MS or PhD in a quantitative field: machine learning, operations research, statistics, applied mathematics, or a related discipline.
• Deep hands-on proficiency in ML: reinforcement learning, supervised and unsupervised methods, probabilistic modeling, and optimization. Experience with AlphaGo-style approaches (self-play, Monte Carlo tree search, policy/value networks) and with large-scale real-time inference systems is a strong plus.
• Genuine user empathy: you think as carefully about what a rider or agency dispatcher experiences as you do about model architecture.
• Very user-minded and clear-eyed about outcomes for users. You work closely with the mobile and SaaS product teams on experimentation, and you know how to find the balance between a delightful user experience and service KPIs such as efficiency and rider experience.
• Business sense and product instincts: you know how to translate a research finding into a shipped product and a shipped product into a business outcome.
• Experience partnering with engineering teams to move models from research into production at scale.
• Track record of working with external customers or partners to iterate on novel solutions in ambiguous, real-world conditions.
• Comfort operating across research, product, and commercial contexts: you can present a model architecture to an ML engineer and a product strategy to a transit authority CEO.
• Passion for building technology that makes public transit work better for everyone, especially riders in underserved communities.