About the role:
Partners with stakeholders to design, develop, optimize, and productionize machine learning (ML) or ML-based solutions and systems that are used within a team to solve complex problems with multiple dependencies. This role also leads team efforts to leverage and improve ML infrastructure for model development, training, deployment needs and scaling ML systems.
About the Team:
The AdTech team enables Uber's growth by enabling acquisition and retention of Uber users through marketing. We are responsible for building and managing systems that improve efficiency and effectiveness of Uber marketing business function. We support various marketing use cases which span Uber's mobile and web applications (Rider, Driver, Eats, Freight, Elevate, U4B etc.), Uber acquisitions (Cornershop, Jump, Careem etc.) through various marketing channels (Search, Social, Programmatic Display, Job boards, AppAds, and as many as 40 more).
Optimization team within AdTech solves automation of intelligent decision making regarding how much money should be invested and spent for different marketing channels. You will build machine learning driven automation systems that will influence multi-million dollar investment decisions. You will consume impressions and click level petabyte scale data, build highly available and highly scalable backend systems flexible enough to handle channel niche complexities and granularities. We are looking for engineers who can come and help define the next generation architecture for marketing at Uber. If you like working with billions of rows of data, 10ms response times, and having a multi-million dollar impact per engineer, we'd like to hear from you!
Minimum qualifications:

• PhD or equivalent in Computer Science, Engineering, Mathematics or related field OR 3-years full-time Software Engineering work experience, WHICH INCLUDES 2-years total technical software engineering experience in one or more of the following areas:
  • Programming language (e.g. C, C++, Java, Python, or Go)
  • 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 2-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:

• Scalable ML architecture
• Feature management

Preferred:

• Previous expertise on distributed systems and/or ML Infra
• Excited about scalability and reliability of systems
• Previous domain expertise in some high-scale Applied ML field (Adtech, Marketing, Fraud/Risk, Cyber Security, Recommendation, search etcs )
• 5+ years of software engineering experience, or 3+ years of software engineering experience with PhD in relevant fields (EE, CS, Stats, Math, etc)
• Experience with causal learning and/or deep learning
• Experience working with large dataset storage systems like NoSQL, HDFS (+Hive) and data distribution systems like Kafka
• Engineering experience in hands-on software development with thoughtfulness of scale, latency and distributed architecture
• A willingness and curiosity to learn both the systems and domain in which you will be solving problem statements
• A great teammate and owner- willing to take on ownership of the systems, and think about operations, maintenance and reliability of his/her systems