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Salary: USD 250,000 - 310,000 / annual
Via is seeking a Head of AI Research Product to lead the intersection of applied machine learning research and product innovation across its rider and transit operations platform. This role operates at the frontier of AI for public transportation, positioning machine learning not as a feature layer but as the core engine that makes transit systems smarter, more equitable, and more efficient.
The role combines three core responsibilities: innovating with lighthouse customers to pilot cutting-edge approaches in real-world transit environments, conducting original applied research, and driving productization of proven methods into Via's platform.
Key responsibilities include:
- Building and leading Via's AI research product function, defining the roadmap for how machine learning advances rider experience and transit network performance
- Designing and deploying reinforcement learning systems that optimize routing, pooling efficiency, and real-time dispatching, balancing rider satisfaction against vehicle utilization and transit agency cost targets
- Pioneering AlphaGo-style machine learning for transit: combining deep reinforcement learning and tree search over high-fidelity simulations of real transit networks to discover dispatching, pooling, and network-design strategies beyond conventional optimization
- Developing weighted ensemble models and multi-objective optimization frameworks that surface tradeoffs between competing system goals: wait times, cost per ride, emissions, and service equity
- Inventing personalization systems that adapt the rider mobile experience in real time, surfacing relevant trip options, proactive alerts, and dynamic pricing signals
- Researching and productizing behavioral incentive models that increase ridership and improve aggregation through surge smoothing, loyalty structures, and gamified engagement grounded in behavioral economics and causal inference
- Partnering with lighthouse transit agencies to co-develop and validate novel AI approaches, turning field pilots into scalable platform features
- Establishing rigorous experimentation infrastructure (A/B testing, contextual bandits, offline policy evaluation) to measure real-world impact of model changes on rider behavior and agency outcomes
Requirements:
- MS or PhD in a quantitative field: machine learning, operations research, statistics, applied mathematics, or 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 large-scale real-time inference systems is a strong plus
- Genuine user empathy and clear-eyed focus on outcomes for riders and agencies
- Business sense and product instincts to translate research findings into shipped products and business outcomes
- 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
- Passion for building technology that makes public transit work better for everyone, especially riders in underserved communities
