Senior Machine Learning Engineer, Customer Obsession

Uber Uber · Consumer · Bangalore, India +1 · Engineering

Senior Machine Learning Engineer for Uber's Customer Obsession team, focusing on enhancing customer support experience and driving cost savings through ML solutions. The role involves designing, developing, and productionizing generative AI, agentic AI, and NLP models, with a strong emphasis on experimental design and collaboration.

What you'd actually do

  1. Design, develop, and productionize machine learning (ML) solutions in the field of customer support engineering spanning generative AI algorithms, agentic AI design at scale, NLP for query understanding and ranking responses, distillation techniques, etc.
  2. Productionize and deploy these models for real-world applications in customer support.
  3. Design and analyze experiments using a combination of data analysis/statistical analysis to lead the team to a reasonable inference.
  4. Review code and designs of teammates, providing constructive feedback.
  5. Collaborate with cross-functional teams to brainstorm new solutions and iterate on the product.

Skills

Required

  • Tensorflow
  • PyTorch
  • JAX
  • Scikit-Learn
  • statistical analysis
  • feature engineering
  • SQL
  • experimental design
  • exploratory data analysis
  • data visualization tools
  • sampling
  • statistical estimates
  • descriptive statistics

Nice to have

  • natural language processing systems

What the JD emphasized

  • Minimum 6 years of experience in industry with a strong focus on machine learning and optimization.
  • Experience in building and owning natural language processing systems over multiple years

Other signals

  • designing systems and algorithms which would enhance the customer support experience
  • unlocking O(100s millions USD) in cost savings
  • leverage your expertise in data analysis, machine learning, and engineering to drive insights
  • identify tech-driven product innovations
  • optimize algorithms and systems ultimately improving user satisfaction and operational efficiency