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Posted about 2 months ago

Machine Learning Engineer - Applied AI

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Job Details

  • Pay: 158,000−158,000 - 175,500 a year
  • Job type: Full-time
  • Location: San Francisco, CA

Full Job Description

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.

The Generative AI team within Applied AI is building a semantic layer to augment our understanding of entities relevant to the Uber platform: places, merchants, items, and riders, and eaters. We work with partners across teams to design, develop and productionize semantic data, features, and embeddings to meet their business needs.

What You'll Do

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

Basic Qualifications

  • 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:
    • Programming language (e.g. C, C++, Java, Python, or Go)
    • Large-scale 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 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.

  • Experience with ML packages such as Tensorflow, PyTorch, JAX, and Scikit-Learn.
  • Experience with big-data architecture, ETL frameworks such as Spark, MapReduce, HDFS, Hive.

Preferred Qualifications

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

Compensation

For San Francisco, CA-based roles: The base salary range for this role is USD158,000peryear−USD158,000 per year - USD175,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD158,000peryear−USD158,000 per year - USD175,500 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 compensation. You will also be eligible for various benefits. More details can be found at the following link: Uber Benefits.