About the Role
At Uber, we are revolutionizing the way people move through our cities. Our Driver Pricing team plays a critical role in this mission by developing and refining the algorithms that determine pricing for our drivers. As a Staff Software Engineer, you will be at the forefront of this innovation, leveraging your expertise in algorithms, optimization, and machine learning to create solutions that balance the needs of drivers, riders, and Uber's business objectives.
This role offers a unique opportunity to work on complex problems with real-world impact, driving efficiency and enhancing user experiences across our platform.
---- What the Candidate Will Do ----
- Design, develop, and optimize algorithms that drive Uber's driver pricing mechanisms, ensuring fairness and efficiency.
- Utilize machine learning techniques to improve the accuracy and responsiveness of pricing models.
- Collaborate with cross-functional teams including data science, product management, and operations to define requirements and deliver robust solutions.
- Conduct thorough testing and validation of algorithms to ensure reliability and scalability in a production environment.
- Analyze and interpret large datasets to extract meaningful insights and inform decision-making.
- Stay current with the latest advancements in algorithms, machine learning, and optimization, and apply this knowledge to ongoing projects.
- Mentor and guide junior engineers, promoting best practices in software development and algorithm design.
- Participate in code reviews, design discussions, and technical brainstorming sessions.
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---- Basic Qualifications ----
- Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field.
- A minimum of 6 years of professional software engineering experience.
- Proven experience in designing and implementing large-scale, distributed systems.
- Strong programming skills in languages such as Python, Java, or C++.
- Deep understanding of algorithm design, optimization techniques, and machine learning principles.
- Experience with data structures, complexity analysis, and software design principles.
- Excellent problem-solving skills and the ability to work in a fast-paced, collaborative environment.
- Strong communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
---- Preferred Qualifications ----
- Master's or Ph.D. in Computer Science, Engineering, Mathematics, or a related field.
- Proficiency with big data technologies such as Hadoop, Spark, or Kafka.
- Knowledge of statistical analysis and predictive modeling.
- Familiarity with modern development tools and methodologies, including Agile and DevOps practices.
- Demonstrated ability to lead and mentor a team of engineers, fostering a culture of collaboration and innovation.
For San Francisco, CA-based roles: The base salary range for this role is USD$218,000 per year - USD$242,000 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.
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.
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.