GenAI Optimization Engineer, Modular

• In-depth knowledge of the Python programming language
• 3+ years of working experience in Machine Learning, Deep Learning, or Generative AI
• Experience implementing framework-level optimizations for Generative AI use cases
• Experience profiling and optimizing GenAI applications
• Deep interest in machine learning technologies and use cases
• Creativity and curiosity for solving complex problems, a team-oriented attitude that enables you to work well with others, and alignment with our culture

Desirable

• ML developers today face significant friction in taking trained models into deployment
• They work in a highly fragmented space, with incomplete and patchwork solutions that require significant performance tuning and non-generalizable/ model-specific enhancements
• At Modular, we are building the next generation AI platform (MAX) that will radically improve the way developers build and deploy AI models
• We’re continuously working to improve the performance and scalability of MAX by extending existing features and adding new features for users to try
• The E2E Optimizations team is responsible for working cross-functionally across the entire Modular tech stack to implement cutting edge optimizations and research for auto-regressive text generation, image generation, and beyond
• Think things like Speculative Decoding, LoRA, Quantization, Chunked Prefill, Distributed Inference, etc
• Design, scope, implement, and tune features for Generative AI use cases in the MAX framework
• Plan and lead cross-functional projects spanning multiple teams and domains
• Collaborate with subject matter experts within Modular to enable features across different parts of the stack
• Contribute to the MAX tech stack across multiple languages, including Mojo, Python, and C++
• Monitor latest research channels and identify potential opportunities for the MAX framework