Uber is looking for a machine learning practitioner to join their Autonomous Optimization team to maximize the marketplace value of autonomous vehicles across Uber's Rides and Delivery platforms. The goal is to pioneer engineering, modeling, and optimization initiatives to bring autonomous vehicles into sustainable, general availability.
Requirements
- 4+ years of experience in the domain of machine learning or backend engineering, or 2+ years if you have a PhD
- 2+ years of experience in one or more of the following areas: machine learning, artificial intelligence, optimization, operational research, or related technical fields
- Knowledge of development and debugging in Java, Scala, or Golang, and experience with scripting languages such as Python and/or shell scripts
- Experience designing, building, and maintaining production machine learning systems
- Experience developing and debugging in large scale data processing frameworks such as Apache Spark, Hive, and/or Presto
- Experience architecting large scale, production software applications
- Experience productionizing applied machine learning solutions towards solving business or product challenges
Responsibilities
- Work on solving complex inferences and optimization problems end-to-end, from problem ideation and model design to productionization
- Design and productionize high-throughput systems to deploy inferences and predictions used by millions of users per day
- Explore novel ideas towards improving the operational efficiency and value of autonomous vehicles and robots across Uber's platforms
- Partner with product managers, scientists, designers, and engineers to develop holistic solutions to real world problems
- Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done
- Have the ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines
Other
- Bachelor's degree or higher in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience
- Highly-motivated, entrepreneurial machine learning practitioner
- Partner with product managers, scientists, designers, and engineers to develop holistic solutions to real world problems
- Own problems end-to-end, and are willing to pick up whatever knowledge you're missing to get the job done
- Have the ability to move fast in an environment where things are sometimes loosely defined and may have competing priorities or deadlines
