SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 250,000 - 310,000 / annual
Via Transportation 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 applications in public transportation, positioning machine learning not as a feature layer but as the core engine driving system intelligence, equity, and efficiency.
The role combines three core responsibilities: innovating with lighthouse customers to pilot and validate cutting-edge approaches in real-world transit environments, conducting original applied research, and driving productization of proven methods into Via's core 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 by 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 in partnership with the rider mobile product team
- Researching and productizing behavioral incentive models that increase ridership and improve demand 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 including A/B testing, contextual bandits, and 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 including 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 thinking about 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, able to present model architecture to ML engineers and product strategy to transit authority executives
- Passion for building technology that makes public transit work better for everyone, especially riders in underserved communities
