**About the Role** We are looking for a passionate and talented Software Engineer to join our Driver Algorithmic Pricing team at Uber. In this role, you will be at the forefront of developing cutting-edge solutions to optimize pricing algorithms that enhance the experience for our drivers and riders. You will work on a variety of technical challenges ranging from algorithm development and machine learning to backend infrastructure and analytics. Your contributions will directly impact the efficiency and effectiveness of our pricing strategies, ensuring that Uber remains a leader in the ride-sharing industry. **\-\-\-\- What the Candidate Will Do ----** - Design, implement, and optimize pricing algorithms that dynamically adjust to market conditions, ensuring fair and efficient pricing for drivers and riders. - Collaborate with data scientists, product managers, and other engineers to develop and deploy machine learning models that enhance our pricing strategies. - Build and maintain scalable algorithms, machine learning models and backend services. - Develop and enhance our analytics and experimentation infrastructure to facilitate data-driven decision-making and continuous improvement of our pricing models. - Contribute to platform and developer/testing infrastructure to ensure robust and reliable delivery of software solutions. - Participate in code reviews, provide constructive feedback, and mentor junior engineers to foster a culture of continuous learning and improvement. - Stay up-to-date with the latest advancements in technology and bring innovative ideas to improve our pricing algorithms and infrastructure. **\-\-\-\- Basic Qualifications ----** - Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field. - 3+ years of experience in software development, with a strong foundation in data structures, algorithms, machine learning, and software design. - Proficiency in at least one programming language such as Java, C++, Python, or Go. - Experience with backend development and infrastructure. - Familiarity with machine learning concepts and applications. - Strong problem-solving skills and the ability to think algorithmically. **\-\-\-\- Preferred Qualifications ----** - Master’s degree or higher in Computer Science, Engineering, or a related field. - 5+ years of experience in software engineering roles. - Experience with optimization techniques and algorithmic development. - Hands-on experience with large-scale distributed systems and microservices architecture. - Knowledge of experimentation infrastructure and A/B testing methodologies. - Proven track record of working on platform and developer/testing infrastructure. - Excellent communication skills and ability to collaborate effectively with cross-functional teams. For San Francisco, CA-based roles: The base salary range for this role is USD$158,000 per year - USD$175,500 per year. You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link [https://www.uber.com/careers/benefits](https://www.uber.com/careers/benefits). Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing [this form](https://forms.gle/aDWTk9k6xtMU25Y5A). Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.