GenAI Optimization Technical Lead, Modular

• In-depth knowledge of the Python programming language
• 6+ years of working experience in Machine Learning, Deep Learning, or Generative AI
• Proven track record of designing and delivering high-performance, scalable systems or frameworks for AI inference and serving
• Experience leading technical teams to execute in challenging problem spaces
• Experience implementing framework-level performance optimizations for Generative AI use cases
• Experience profiling and reducing latency in 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
• CUDA/ROCM/Accelerator Programming and Optimization experience
• For candidates who fall outside of the listed requirements, we nevertheless encourage you to apply as we may have openings that are lower/higher level than the ones advertised

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
• The Serve 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
• Serve as Technical Leader for a team of cross-functional engineers optimizing Generative AI use cases
• Mentor and contribute to the growth of other engineers
• Define and drive the technical roadmap for performance architecture for the MAX Serve SDK
• Define, plan, and technically lead major cross-functional projects spanning multiple teams and domains to drive core performance and scalability wins
• Serve as a primary technical liaison, collaborating with subject matter experts across Modular to align on performance features and stack integration
• 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