Senior Staff Machine Learning Engineer - Rider Pricing

About the role

Leads efforts within the organization to drive the design, development, optimization, and productionization of ML or ML-based solutions and systems that are used to solve strategically important problems. This role is also responsible for defining and driving the improvement of key ML infrastructure for model development, training, deployment needs and scaling ML systems.

It is a challenging yet rewarding job. You will have a lot of opportunities to work with product managers, data scientists and of course engineers from other teams. You will participate in the whole development cycle of a software product from product scoping, architecture design, software implementation, to productionisation, and learn how to iterate a product for greater success. We own a few products that directly impact Uber's top and bottom lines.

About the Team

Uber Marketplace ( ~~~/ ) is at the core of Uber's business, and Rides Trip Pricing is a strategically critical component of Marketplace. The mission of the team is to foster growth and increase profitability of Uber by pushing the frontiers of machine learning, data science and economics and developing highly reliable and scalable platforms to accelerate Uber's impact on the transportation industry.

The team is rapidly growing with high impact and visibility from the top. We are responsible for developing state of the art technology to optimize the pricing and incentives strategy in our platform that directly drives efficiencies and effectiveness across user interactions with Uber. We handle a significant trunk of all the pricing and incentives budget we are spending at Uber and we aim to build long-term loyalty among Uber users and create a healthy and balanced ecosystem.

Minimum qualifications

• PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 4-years full-time Software Engineering work experience OR 7-years full-time Software Engineering work experience, WHICH INCLUDES 4-years total technical software engineering experience in one or more of the following areas:

• Programming language (e.g. C, C++, Java, Python, or Go)

• Large-scale training using data structures and algorithms

• Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)

• Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib

• Note the 4-years total of specialized software engineering experience may have been gained through education and full-time work experience, additional training, coursework, research, or similar (OR some combination of these). The years of specialized experience are not necessarily in addition to the years of Education & full-time work experience indicated.

Technical skills

Required

• Deep Learning

• Scalable ML architecture

• Experience in applying machine learning models to solve large-scale real-world problems

Preferred

• Personalization, user understanding and targeting

• Optimization (RL/Bayes/Bandits)

• Causal inference

For San Francisco, CA-based roles: The base salary range for this role is USD$252,000 per year - USD$280,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$252,000 per year - USD$280,000 per year. For all US locations, 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 ~~~.

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- ~~~