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Description
About the role
Here, every ride order flows through our matching system. The quality of each allocation shapes platform-wide outcomes, affecting how quickly riders are picked up, how efficiently drivers earn, and how well we manage supply and demand across dozens of markets. We are evolving our matching stack from a heuristic-based dispatch system toward a context-aware, probability-based allocation engine powered by machine learning and reinforcement learning. As Principal Product Manager, you will own this transition end to end. You will define the roadmap, build a cross-functional pod of engineers, data scientists, and specialists, and be accountable for both the performance of the existing system and the delivery of the next generation. This is a deeply technical role that also requires clear, precise communication with senior stakeholders. You will own the interface between matching and pricing, coordinate experiment rollout across markets, and set the direction for how Bolt allocates supply at scale.
Main tasks and responsibilities:
Define the product strategy and roadmap for the dispatching engine, sequencing foundational ML/RL investments alongside near-term improvements to queue ranking, dispatch strategy, and retry governance.
Coordinate the productionisation of machine learning and reinforcement learning models, from offline training through shadow mode, A/B testing, incremental rollout, and live monitoring across multiple markets.
Own and improve the performance of the existing matching stack while the new engine is being built, maintaining stability and delivering incremental efficiency gains.
Define and maintain the signal contract between matching and pricing, ensuring matching delivers reliable, low-latency completion probability and supply density signals.
Design experiments, evaluate results, and translate complex algorithmic trade-offs into clear recommendations for senior stakeholders and cross-functional peers.
Set priorities and resolve trade-offs within a cross-functional pod spanning product, engineering, data science, and specialist roles, acting as the accountable owner for matching KPIs.
About you:
You have experience shipping machine learning models into production systems that serve real-time allocations at scale, including ownership of the full lifecycle from feature engineering and training through shadow deployment, monitoring, and iteration.
You have worked on allocation, matching, dispatch, or equivalent real-time systems in a two-sided marketplace, logistics platform, or high-throughput environment, with a strong grasp of supply-demand dynamics and global versus local optimisation trade-offs.
You have navigated the complexity of maintaining a live production system while simultaneously building its replacement, with a track record of keeping current performance stable through a migration.
You have coordinated cross-functional product pods and have experience working across organisational boundaries, including negotiating interface contracts and mutual accountability with adjacent teams such as pricing or economics.
You are able to move fluently between detailed statistical evaluation and clear stakeholder communication, and you produce high-quality written deliverables including strategy documents, technical specifications, and experiment reviews.
You have direct exposure to reinforcement learning in production, or have worked with teams that deployed RL systems, with a solid grasp of reward design, exploration-exploitation trade-offs, and the gap between simulation and live environments.
Are you interested in this position?
Apply by clicking on the “Apply Now” Button below!
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