Mosaic AI is hiring experienced machine learning platform engineers to build out our generative AI platform for the ML model development lifecycle including pre-training of large language models (LLMs), fine-tuning, evaluation, and serving. 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 such as our CLI and SDK interfaces, backend APIs, distributed orchestration systems built on Kubernetes, and core platform infrastructure to support our product.

You will:

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 ML researchers in the company and customers 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 as well as own the implementation of these designs

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

5+ years of full time industry experience

Strong software engineering skills

Experience building large-scale distributed systems

Experience building ML platform systems for applications in the ML model development lifecycle such as model training, data preparation, 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.