Responsibilities
- Lead the design, development, and productionization of end-to-end ML solutions for large-scale distributed systems serving billions of trips
- Develop novel pricing approaches for online marketplaces combining machine learning, algorithmic game theory, and optimization to provide earnings opportunities for millions of drivers
- Collaborate with the team leads to set the team's technical direction and own its implementation, providing technical mentorship to junior engineers
- Work with a team of engineers, product managers, and scientists to design and deliver high-impact technical solutions to complex business problems
Basic qualifications
- Ph.D., M.S. or Bachelor's degree in Computer Science, Machine Learning, or Operations Research, or equivalent technical background with exceptional demonstrated impact
- 4+ years of experience in developing and deploying machine learning models and optimization algorithms in large-scale production environments, delivering measurable business impact over multiple quarters and making significant technical contributions
- Proficiency in programming languages such as Python, Scala, Java, or Go
- Experience with large-scale data systems (e.g. Spark, Ray), real-time processing (e.g. Flink), and microservices architectures
- Experience in the development, training, productionization and monitoring of ML solutions at scale, ranging from offline pipelines to online serving and MLOps
- Deep understanding of modern ML algorithms (e.g. DNNs, multi-task models, transformers) and mathematical optimization (e.g. LP, convex optimization)
Preferred qualifications
- Experience in developing and deploying pricing algorithms for multi-sided real-time marketplaces with strategic agent behavior
- Experience leading complex technical projects and influencing the scope and output of others
- Track record of translating ambiguous business problems into technical solutions and driving multi-functional projects
- Excellent communication skills to lead initiatives and collaborate effectively with cross-functional partners
- Experience in reinforcement learning and causal machine learning
Tags & focus areas
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