Required Staff ML Engineer
Tel Aviv
As a Staff ML Engineer, you will play a central role in shaping how millions of riders and drivers move through our cities every day. Our team sits at the intersection of machine learning, optimization, and real-world operations – turning complex, multi-dimensional challenges into the real-time intelligence that powers our mass-scale automated dispatch system. This is a rare opportunity to work on problems that are genuinely hard, at a scale that is genuinely rare, where the solutions you build have a direct and visible impact on the efficiency and reliability of transit networks around the world.
About the Role:
Own the development of ML models and optimization algorithms that drive our real-time dispatch system – making smart, scalable decisions across thousands of simultaneous rides, drivers, and operational constraints.
Design and implement online algorithms for real-time decision-making, balancing system utilization with a consistently high quality of service for riders – where every millisecond and every percentage point of efficiency matters.
Model and mathematically represent competing demands on our system, translating messy real-world operational complexity into elegant, tractable formulations that can be solved at scale.
Use sophisticated statistical methods to analyse demand patterns, traffic dynamics, and fleet performance – generating insights that directly inform algorithm development and operational strategy.
Collaborate closely with engineering, product, and operations teams to bring complex algorithmic work to life in production – owning the full journey from research and prototyping through to real-world deployment and iteration.
Tel Aviv
As a Staff ML Engineer, you will play a central role in shaping how millions of riders and drivers move through our cities every day. Our team sits at the intersection of machine learning, optimization, and real-world operations – turning complex, multi-dimensional challenges into the real-time intelligence that powers our mass-scale automated dispatch system. This is a rare opportunity to work on problems that are genuinely hard, at a scale that is genuinely rare, where the solutions you build have a direct and visible impact on the efficiency and reliability of transit networks around the world.
About the Role:
Own the development of ML models and optimization algorithms that drive our real-time dispatch system – making smart, scalable decisions across thousands of simultaneous rides, drivers, and operational constraints.
Design and implement online algorithms for real-time decision-making, balancing system utilization with a consistently high quality of service for riders – where every millisecond and every percentage point of efficiency matters.
Model and mathematically represent competing demands on our system, translating messy real-world operational complexity into elegant, tractable formulations that can be solved at scale.
Use sophisticated statistical methods to analyse demand patterns, traffic dynamics, and fleet performance – generating insights that directly inform algorithm development and operational strategy.
Collaborate closely with engineering, product, and operations teams to bring complex algorithmic work to life in production – owning the full journey from research and prototyping through to real-world deployment and iteration.
Requirements:
Advanced degree (M.Sc. or PhD) in Computer Science, Mathematics or a closely related field, with a strong background in Machine Learning.
8+ years of industry experience shipping machine learning models at production scale – you've taken hard problems from whiteboard to deployment and know what it takes to make research work in the real world.
Deep, hands-on expertise in Machine Learning, with significant experience in reinforcement learning for complex, dynamic or constrained systems.
Excellent coding skills in Python or similar, with the ability to turn rigorous ideas into working, maintainable solutions that perform under real operational load.
Strong applied research mindset: able to translate ambiguous business/operational challenges into tractable ML formulations and measurable impact.
Naturally curious, fast-learning, and collaborative – you bring strong communication skills and genuine intellectual generosity to the teams you work with.
Advanced degree (M.Sc. or PhD) in Computer Science, Mathematics or a closely related field, with a strong background in Machine Learning.
8+ years of industry experience shipping machine learning models at production scale – you've taken hard problems from whiteboard to deployment and know what it takes to make research work in the real world.
Deep, hands-on expertise in Machine Learning, with significant experience in reinforcement learning for complex, dynamic or constrained systems.
Excellent coding skills in Python or similar, with the ability to turn rigorous ideas into working, maintainable solutions that perform under real operational load.
Strong applied research mindset: able to translate ambiguous business/operational challenges into tractable ML formulations and measurable impact.
Naturally curious, fast-learning, and collaborative – you bring strong communication skills and genuine intellectual generosity to the teams you work with.
This position is open to all candidates.
