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Principal Product Manager, Rides Matching
HíbridoTiempo completoGestión de productoMobilitySoftware Development
Lead the evolution of a rides matching system from heuristic dispatch to a machine learning and reinforcement learning-based allocation engine. Own the product strategy, roadmap, and a cross-functional pod of engineers and data scientists, driving performance and innovation in a global mobility platform.
Responsabilidades
- 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.
Requisitos
- 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.
- Experience working 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.
- Track record of maintaining a live production system while simultaneously building its replacement, keeping current performance stable through a migration.
- Experience coordinating cross-functional product pods and working across organisational boundaries, including negotiating interface contracts and mutual accountability with adjacent teams such as pricing or economics.
- Ability to move fluently between detailed statistical evaluation and clear stakeholder communication, producing high-quality written deliverables including strategy documents, technical specifications, and experiment reviews.
- Direct exposure to reinforcement learning in production, or experience working 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.
Beneficios
- Accelerate your professional growth with unique career opportunities.
- Enjoy a rewarding salary and stock options.
- Take care of your physical and mental health with wellness perks.
- Celebrate 5 years with a 1-month paid sabbatical.
- Connect with colleagues at annual company events and smaller team gatherings.
- Balance flexibility and in-person collaboration with a hybrid model, including at least 12 monthly in-office days.