We’re presently seeking people with these qualities to join Uber's Map Automation subteam, a team that uses our extensive imagery and trace data for mapping applications to build data pipelines that extract valuable bits of information from imagery and GPS data in order to optimize and augment maps. This will have a direct impact on both riders and drivers by helping them connect faster and more efficiently. We are looking for a seasoned software engineer who has a solid grasp of machine learning theory and statistical inference - someone who is able to whiteboard some theory while at the same time is able to roll up his/her sleeves and get coding. We expect the person to be able to read and understand research papers and be able to translate the ideas into efficient code. We also expect the person to be data oriented - i.e. be able to set up experiments to measure things that will in turn drive decisions.
Successful candidates should have:
Strong programming skills in either Java, C++, or Python
Experience with developing big-data processing pipelines over Hadoop or similar technologies.
Experience with developing systems for detection, recognition, classification, entity-matching, etc.
Experience with machine learning/statistical inferencing technologies such as random forests, deep nets., Bayesian nets, etc.
Experience in mapping, or computational geometry is a plus.
A Ph.D. in a quantitative field preferred but not required.
We’re presently seeking people with these qualities to join Uber's Map Automation subteam, a team that uses our extensive imagery and trace data for mapping applications to build data pipelines that extract valuable bits of information from imagery and GPS data in order to optimize and augment maps. This will have a direct impact on both riders and drivers by helping them connect faster and more efficiently. We are looking for a seasoned software engineer who has a solid grasp of machine learning theory and statistical inference - someone who is able to whiteboard some theory while at the same time is able to roll up his/her sleeves and get coding. We expect the person to be able to read and understand research papers and be able to translate the ideas into efficient code. We also expect the person to be data oriented - i.e. be able to set up experiments to measure things that will in turn drive decisions.
Successful candidates should have:
Strong programming skills in either Java, C++, or Python Experience with developing big-data processing pipelines over Hadoop or similar technologies. Experience with developing systems for detection, recognition, classification, entity-matching, etc. Experience with machine learning/statistical inferencing technologies such as random forests, deep nets., Bayesian nets, etc. Experience in mapping, or computational geometry is a plus. A Ph.D. in a quantitative field preferred but not required.