Machine Learning Platform Engineer, Generative AI

Summary

Databricks Mosaic AI is hiring experienced machine learning platform engineers to build out our customer-facing generative AI platform for the ML development lifecycle including data generation, training, evaluation, serving, and agent-building.

You will thrive in this role if you have a strong sense of end-to-end ownership and enjoy translating user requirements into product interfaces and building the backend distributed systems to power those interfaces. In this role, you will have the opportunity to contribute to all areas of our stack spanning from user-facing features to low-level GPU orchestration.

Responsibilities

• 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
• Work closely with both customers and internal ML researchers to identify key areas of development for our generative AI platform
• Have strong end-to-end product ownership, translating product requirements into user interfaces and backend distributed system design and own end-to-end implementation
• Design and build the core platform infrastructure that supports our customer-facing product features
• Ensure the reliability, security, and scalability of the backend distributed systems that power all aspects of our product

Requirements

• 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
• Direct experience developing ML models is a plus but not required
• 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.

We value candidates who are curious about all parts of the company's success and are willing to learn new technologies along the way.