The matching team directly contributes to Uber’s growth and profitability by intelligently optimizing dispatch decisions. The team is dealing with a high-scale realtime backend system that’s solving a complex mathematical optimization problem using machine learning.
- Translate business level metrics to an engineering/science problem
- Solving the complicated optimization problem by combining a highly scalable backed system and machine learning models.
- Be responsible for the End to End of the product – ML model pipeline & backend system design, implementation, AB testing, and rollout.
- Collaborating in a team environment across all functions, including but not limited to engineers, product managers, data scientists, operations
- BS, MS, or PhD in Computer Science, Math or a related technical field, or equivalent experience.
- 2+ years of experience in software engineering focusing on prediction and optimization problems.
- Sound understanding of computer architecture and CS fundamentals.
- Proficient in one of the following programming languages: Java, Go, Python, C/C++.
Qualification & Experience:
- Detailed problem-solving approach and knowledge of algorithms, data structures, and complexity analysis.
- Experience working on large-scale distributed systems
- Experience working on large scale Machine Learning platforms,
- Grit, drive and a strong feeling of ownership coupled with collaboration
- Advanced degree in Computer Science and related field.
- Engineering work, internships, relevant course-work, or project experience in any of the following areas: machine learning, search, ranking, recommendation systems, pattern recognition, data mining, or artificial intelligence
- Proven experience developing sophisticated software systems scaling to millions of users with production quality deployment, monitoring and reliability
- Proven track record to translate insight into business recommendations.
- Strong engineering and science skills.
Vacancy Type: Full Time
Job Location: San Francisco, CA, US
Application Deadline: N/A
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