Senior Machine Learning Engineer - Genai Platform

Databricks Databricks · Data AI · San Francisco, CA · Engineering - Pipeline

Hiring experienced machine learning platform engineers to build out a customer-facing generative AI platform for the ML development lifecycle, including data generation, training, evaluation, serving, and agent-building. The role involves end-to-end ownership, translating user requirements into product interfaces, and building backend distributed systems. Responsibilities span from user-facing features to low-level GPU orchestration.

What you'd actually do

  1. Play a key role in the end-to-end design and implementation of our product which is a platform for powering use cases across training and serving of generative AI models
  2. Work closely with both customers and internal ML researchers to identify key areas of development for our generative AI platform
  3. Have strong end-to-end product ownership, translating product requirements into user interfaces and backend distributed system design and own end-to-end implementation
  4. Design and build the core platform infrastructure that supports our customer-facing product features
  5. Ensure the reliability, security, and scalability of the backend distributed systems that power all aspects of our product

Skills

Required

  • 4+ years of hands-on programming experience with at least one modern language such as Python, Scala, Go, or C++
  • Strong sense of distributed systems design and experience building large-scale systems
  • Experience building ML platform systems for applications in the ML model development lifecycle such as data preparation, model training, model evaluation, and model serving

Nice to have

  • Direct experience developing ML models is a plus
  • Strong sense of end-to-end product ownership as well as intuition for both robust system design and product usability
  • Effective communication skills and the ability to articulate complex technical ideas to cross-disciplinary internal and external stakeholders.

What the JD emphasized

  • end-to-end design and implementation
  • end-to-end product ownership
  • backend distributed systems
  • customer-facing generative AI platform

Other signals

  • customer-facing generative AI platform
  • ML development lifecycle
  • training and serving of generative AI models
  • backend distributed systems
  • low-level GPU orchestration