Principal Product Manager (Rides Matching)

TL;DR

Principal Product Manager (Rides Matching): Own the transition of Bolt’s ride matching from a heuristic dispatch system to a context-aware, probability-based allocation engine with an accent on productionizing machine learning and reinforcement learning for real-time allocations at scale. Focus on end-to-end roadmap delivery, cross-functional pod execution, and translating complex algorithmic trade-offs into clear decisions for senior stakeholders while maintaining stability of the existing matching stack.

Company

Bolt is a global mobility platform helping people move through cities with ride-hailing and other transportation services.

What you will do

• Define product strategy and roadmap for the dispatching engine, sequencing ML/RL investments with improvements to queue ranking, dispatch strategy, and retry governance.
• Coordinate productionization of ML/RL models from offline training through shadow mode, A/B testing, incremental rollout, and live monitoring across multiple markets.
• Own and improve performance of the existing matching stack during the migration, maintaining stability and delivering incremental efficiency gains.
• Define and maintain the signal contract between matching and pricing to deliver reliable, low-latency completion probability and supply density signals.
• Design experiments, evaluate results, and translate algorithmic trade-offs into recommendations for senior stakeholders and cross-functional peers.
• Set priorities and resolve trade-offs within a cross-functional pod, acting as the accountable owner for matching KPIs.

Requirements

• Experience shipping machine learning models into production systems that serve real-time allocations at scale, including full lifecycle ownership (feature engineering, training, shadow deployment, monitoring, iteration).
• Experience with allocation, matching, dispatch, or equivalent real-time systems in a two-sided marketplace, logistics platform, or high-throughput environment, with strong understanding of supply-demand dynamics and global vs local optimization trade-offs.
• Proven ability to maintain a live production system while building its replacement, including keeping current performance stable through migration.
• Experience coordinating cross-functional product pods and working across organizational boundaries, including negotiating interface contracts and mutual accountability with adjacent teams (e.g., pricing/economics).
• Ability to move between detailed statistical evaluation and clear stakeholder communication, producing high-quality written deliverables (strategy documents, technical specifications, experiment reviews).
• Direct exposure to reinforcement learning in production, including reward design, exploration–exploitation trade-offs, and understanding the gap between simulation and live environments.

Culture & Benefits

• Hybrid model with at least 12 monthly in-office days.
• Wellness perks for physical and mental health.
• Salary and stock options, with additional sabbatical benefit (1-month paid sabbatical after 5 years).
• Annual company events and smaller team gatherings.
• Using AI daily is a baseline expectation, with high standards for output quality.

Hiring process

• Complete the application form and submit required fields marked with an asterisk.

Location: Tallinn - HQ, Estonia