As a Senior Machine Learning Engineer, you will collaborate with customers 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 toimprove ML infrastructure for model development, training, deployment needs and scaling ML systems.
This role is on the TripContext team within the Maps organization. As a Senior Machine Learning Engineer on the TripContext team, you will be working with a wide range of sensor data (IMU, GPS, audio, etc...) and analytic events to develop inferences that enable some of the most important parts of Uber's business - safety, pickup experience, drop-off experience, Uber Eats delivery and more.
We create meaningful insights that our partner product teams (Rider, Driver, Eats, Safety et al) use to improve customer and trip experiences. We do this by researching new models and algorithms and building platforms to serve our insights to customers at Uber scale. Our systems stream more than 10 TB of data a day, producing inferences like crash detection and other trip events that improve the Uber experience for riders, drivers, eaters, and couriers. You will be working with some of the world's most experienced engineering professionals, data scientists, and research scientists on user-facing products with global impact. This is your chance to develop ground breaking technology that will touch every Uber trip!
---- What the Candidate Will Do ----
• Explore our massive dataset of sensor data from GPS, IMU, barometer, etc to discover opportunities to improve Uber's product. • Research and develop machine learning models (crash detection, phone handling, harsh braking, trip events) that provide insight into trips and customer experiences.• Work closely with backend infrastructure engineers to architect and build the pipelines to train and serve the machine learning models at Uber scale. • Work closely with PMs and engineers on partner teams to integrate and validate systems end to end.
---- Basic Qualifications ----
• Masters degree in Computer Science, Engineering, Mathematics or related field OR 5-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)• Algorithms and data structures• 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• Signal Processing algorithm development techniques
---- Preferred Qualifications ----
• Experience in stream processing -- Storm, Spark, Flink, etc• Experience in working with sensor or other time-series data (audiovisual, GPS, IMU, etc)• Experience building large scale distributed systems• At least five (5) years of software engineering experience and building production scale ML models• Strong communication skills• Demonstrated leadership skill set
At Uber, we ignite opportunity by setting the world in motion. We take on big problems to help drivers, riders, delivery partners, and eaters get moving in more than 10,000 cities around the world.
We welcome people from all backgrounds who seek the opportunity to help build a future where everyone and everything can move independently. If you have the curiosity, passion, and collaborative spirit, work with us, and let's move the world forward, together.
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.