Machine Learning Engineer - Applied AI

About the Role

The Applied AI team collaborates with product teams across Uber to deliver innovative AI solutions for core business problems. We work closely with engineering, product and data science teams to understand core business problems and the potential for AI solutions, then deliver those AI solutions end-to-end. Key areas of expertise include Computer Vision, ML Optimization, Geospatial AI, Personalization and Generative AI.

---- What the Candidate Will Do ----

1. Build and iterate on capturing semantic information of Uber entities by leveraging LLMs.
2. Generate embeddings using the semantic information to help improve our understanding of places, merchants, items and users.
3. Leverage this to improve ML models across Uber and to build novel personalized experiences.

---- Basic Qualifications ----

1. PhD or equivalent in Computer Science, Engineering, Mathematics or related field AND 2-years full-time Software Engineering work experience OR 5-years full-time Software Engineering work experience, WHICH INCLUDES 3-years total technical software engineering experience in one or more of the following areas:
2. Programming language (e.g. C, C++, Java, Python, or Go)
3. Large-scale training using data structures and algorithms
4. Modern machine learning algorithms (e.g., tree-based techniques, supervised, deep, or probabilistic learning)
5. Machine Learning Software such as Tensorflow/Pytorch, Caffe, Scikit-Learn, or Spark MLLib
6. Note the 3-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.
7. Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
8. Experience with big-data architecture, ETL frameworks such as Spark, MapReduce, HDFS, Hive.

---- Preferred Qualifications ----

1. 3+ years of experience in the development, training, productionization and monitoring of ML solutions at scale.
2. Experience with formulating a business problem as an ML problem, identifying the right features, model structure and optimization constraints, and delivering business impact.
3. Experience in modern deep learning architectures and recommender systems.
4. Experience in building foundational data and embeddings that can be plugged into other application specific models.
5. Experience working with multiple across team and org boundaries with engineering and product counterparts.

For San Francisco, CA-based roles: The base salary range for this role is USD$158,000 per year - USD$175,500 per year.

For Sunnyvale, CA-based roles: The base salary range for this role is USD$158,000 per year - USD$175,500 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 https://www.uber.com/careers/benefits.

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.

Offices continue to be central to collaboration and Uber’s cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.